ALMASWA_BoT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd985fe6-3ce9-4648-a841-37f7f0434aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import time\n",
    "import psutil\n",
    "import os\n",
    "import time\n",
    "import tracemalloc\n",
    "import psutil\n",
    "import sys\n",
    "from pympler import asizeof\n",
    "import matplotlib.pyplot as plt\n",
    "from river import imblearn\n",
    "from scipy.stats import ttest_rel\n",
    "import itertools\n",
    "\n",
    "from river import compose, preprocessing, metrics, stream, linear_model, evaluate\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from ALMASWA import ALMASWAClassifier\n",
    "\n",
    "from collections import Counter\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from Hamming import HammingLoss\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b49c779d-0d63-4461-be17-8c0e96b0c2e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IPV4_SRC_ADDR</th>\n",
       "      <th>L4_SRC_PORT</th>\n",
       "      <th>IPV4_DST_ADDR</th>\n",
       "      <th>L4_DST_PORT</th>\n",
       "      <th>PROTOCOL</th>\n",
       "      <th>L7_PROTO</th>\n",
       "      <th>IN_BYTES</th>\n",
       "      <th>OUT_BYTES</th>\n",
       "      <th>IN_PKTS</th>\n",
       "      <th>OUT_PKTS</th>\n",
       "      <th>TCP_FLAGS</th>\n",
       "      <th>FLOW_DURATION_MILLISECONDS</th>\n",
       "      <th>Label</th>\n",
       "      <th>Attack</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>192.168.100.6</td>\n",
       "      <td>52670</td>\n",
       "      <td>192.168.100.1</td>\n",
       "      <td>53</td>\n",
       "      <td>17</td>\n",
       "      <td>5.212</td>\n",
       "      <td>71</td>\n",
       "      <td>126</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4294966</td>\n",
       "      <td>0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>192.168.100.6</td>\n",
       "      <td>49160</td>\n",
       "      <td>192.168.100.149</td>\n",
       "      <td>4444</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000</td>\n",
       "      <td>217753000</td>\n",
       "      <td>199100</td>\n",
       "      <td>4521</td>\n",
       "      <td>4049</td>\n",
       "      <td>24</td>\n",
       "      <td>4176249</td>\n",
       "      <td>1</td>\n",
       "      <td>Theft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>192.168.100.46</td>\n",
       "      <td>3456</td>\n",
       "      <td>192.168.100.5</td>\n",
       "      <td>80</td>\n",
       "      <td>17</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8508021</td>\n",
       "      <td>8918372</td>\n",
       "      <td>9086</td>\n",
       "      <td>9086</td>\n",
       "      <td>0</td>\n",
       "      <td>4175916</td>\n",
       "      <td>0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>192.168.100.3</td>\n",
       "      <td>80</td>\n",
       "      <td>192.168.100.55</td>\n",
       "      <td>8080</td>\n",
       "      <td>6</td>\n",
       "      <td>7.000</td>\n",
       "      <td>8442138</td>\n",
       "      <td>9013406</td>\n",
       "      <td>9086</td>\n",
       "      <td>9086</td>\n",
       "      <td>0</td>\n",
       "      <td>4175916</td>\n",
       "      <td>0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192.168.100.46</td>\n",
       "      <td>80</td>\n",
       "      <td>192.168.100.5</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>7.000</td>\n",
       "      <td>8374706</td>\n",
       "      <td>0</td>\n",
       "      <td>9086</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4175916</td>\n",
       "      <td>0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    IPV4_SRC_ADDR  L4_SRC_PORT    IPV4_DST_ADDR  L4_DST_PORT  PROTOCOL  \\\n",
       "0   192.168.100.6        52670    192.168.100.1           53        17   \n",
       "1   192.168.100.6        49160  192.168.100.149         4444         6   \n",
       "2  192.168.100.46         3456    192.168.100.5           80        17   \n",
       "3   192.168.100.3           80   192.168.100.55         8080         6   \n",
       "4  192.168.100.46           80    192.168.100.5           80         6   \n",
       "\n",
       "   L7_PROTO   IN_BYTES  OUT_BYTES  IN_PKTS  OUT_PKTS  TCP_FLAGS  \\\n",
       "0     5.212         71        126        1         1          0   \n",
       "1     0.000  217753000     199100     4521      4049         24   \n",
       "2     0.000    8508021    8918372     9086      9086          0   \n",
       "3     7.000    8442138    9013406     9086      9086          0   \n",
       "4     7.000    8374706          0     9086         0          0   \n",
       "\n",
       "   FLOW_DURATION_MILLISECONDS  Label  Attack  \n",
       "0                     4294966      0  Benign  \n",
       "1                     4176249      1   Theft  \n",
       "2                     4175916      0  Benign  \n",
       "3                     4175916      0  Benign  \n",
       "4                     4175916      0  Benign  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2a26d8c-4aed-46c1-abea-2b3e0117ab13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({'Reconnaissance': 470655, 'DDoS': 56844, 'DoS': 56833, 'Benign': 13859, 'Theft': 1909})\n"
     ]
    }
   ],
   "source": [
    "print('Original dataset shape  %s' % Counter(data['Attack']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "474b1eba-f47c-45e8-a447-bdfbb3fb5459",
   "metadata": {},
   "source": [
    "## Experiment 1: Comparing ALMA with ALMASWA on the complete dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "91232c56-2804-44da-b8d2-2e06d736dcb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9240\n",
      "Recall: 0.9349\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9821\n",
      "Recall: 0.9945\n"
     ]
    }
   ],
   "source": [
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT.csv\")\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'Recall': metrics.Recall()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    for name, model in classifiers.items():\n",
    "        y_pred = model.predict_one(x)\n",
    "        model.learn_one(x, true_y)\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['Recall'].update(true_y, y_pred)\n",
    "\n",
    "# Display the results\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"Recall: {metric['Recall'].get():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1590c94-e57b-4022-804d-ad010a1403df",
   "metadata": {},
   "source": [
    "## Experiment 2: Comparing ALMA with ALMASWA on different attack classes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b3e1c6d-bc12-4d42-aa59-9e21faed1bfc",
   "metadata": {},
   "source": [
    "### DDoS attack class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9816e33-ee7e-44b4-bef7-f450e963d74b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({'DDoS': 56844, 'Benign': 13859})\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9870\n",
      "Recall: 0.9923\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9963\n",
      "Recall: 0.9980\n"
     ]
    }
   ],
   "source": [
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT-DDoS.csv\")\n",
    "print('Original dataset shape  %s' % Counter(data['Attack']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'Recall': metrics.Recall()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    for name, model in classifiers.items():\n",
    "        y_pred = model.predict_one(x)\n",
    "        model.learn_one(x, true_y)\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['Recall'].update(true_y, y_pred)\n",
    "\n",
    "# Display the results\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"Recall: {metric['Recall'].get():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6365faf6-40e4-4a46-bc52-894a04e8cd19",
   "metadata": {},
   "source": [
    "### DoS attack class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a21abe44-8bb6-477e-8b90-4349311b4567",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({'DoS': 56833, 'Benign': 13859})\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9876\n",
      "Recall: 0.9926\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9958\n",
      "Recall: 0.9978\n"
     ]
    }
   ],
   "source": [
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT-DoS.csv\")\n",
    "print('Original dataset shape  %s' % Counter(data['Attack']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'Recall': metrics.Recall()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    for name, model in classifiers.items():\n",
    "        y_pred = model.predict_one(x)\n",
    "        model.learn_one(x, true_y)\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['Recall'].update(true_y, y_pred)\n",
    "\n",
    "# Display the results\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"Recall: {metric['Recall'].get():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "760e7b51-a1b8-46a8-b208-6f4e5e4630be",
   "metadata": {},
   "source": [
    "### Reconnaissance attack class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "26e9008a-058a-443c-b090-b815eb7c7aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({'Reconnaissance': 470655, 'Benign': 13859})\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9496\n",
      "Recall: 0.9626\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9820\n",
      "Recall: 0.9975\n"
     ]
    }
   ],
   "source": [
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT-Recon.csv\")\n",
    "print('Original dataset shape  %s' % Counter(data['Attack']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'Recall': metrics.Recall()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    for name, model in classifiers.items():\n",
    "        y_pred = model.predict_one(x)\n",
    "        model.learn_one(x, true_y)\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['Recall'].update(true_y, y_pred)\n",
    "\n",
    "# Display the results\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"Recall: {metric['Recall'].get():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca37a458-176f-4e70-83c8-f32bdadfe199",
   "metadata": {},
   "source": [
    "### Theft attack class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd61bf7e-3fc4-4675-aa5b-0c2b16074e87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({'Benign': 13859, 'Theft': 1909})\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.6586\n",
      "Recall: 0.7627\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.8326\n",
      "Recall: 0.9141\n"
     ]
    }
   ],
   "source": [
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT-Theft.csv\")\n",
    "print('Original dataset shape  %s' % Counter(data['Attack']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'Recall': metrics.Recall()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    for name, model in classifiers.items():\n",
    "        y_pred = model.predict_one(x)\n",
    "        model.learn_one(x, true_y)\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['Recall'].update(true_y, y_pred)\n",
    "\n",
    "# Display the results\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"Recall: {metric['Recall'].get():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baae0067-0100-4818-9f1a-44320d9d5675",
   "metadata": {},
   "source": [
    "## Checking for features to use for introduction "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3780cb74-7531-45f6-815b-17bab14af008",
   "metadata": {},
   "source": [
    "### Statistical characteristic of each feature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a01c4391-6baa-4380-8f59-7bb4659df149",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Distribution Summary:\n",
      "                               count          mean           std   min  \\\n",
      "L4_SRC_PORT                 600100.0  4.652890e+04  1.203639e+04   0.0   \n",
      "L4_DST_PORT                 600100.0  7.949494e+03  1.410115e+04   0.0   \n",
      "PROTOCOL                    600100.0  6.584854e+00  2.567061e+00   1.0   \n",
      "L7_PROTO                    600100.0  8.956318e+00  3.496943e+01   0.0   \n",
      "IN_BYTES                    600100.0  9.222368e+03  4.734768e+05  28.0   \n",
      "OUT_BYTES                   600100.0  6.997822e+03  8.214194e+05   0.0   \n",
      "IN_PKTS                     600100.0  1.243389e+01  2.460042e+02   1.0   \n",
      "OUT_PKTS                    600100.0  5.696411e+00  1.914214e+02   0.0   \n",
      "TCP_FLAGS                   600100.0  2.185652e+01  8.114914e+00   0.0   \n",
      "FLOW_DURATION_MILLISECONDS  600100.0  3.468594e+06  1.665532e+06   0.0   \n",
      "\n",
      "                                  25%        50%        75%          max  \\\n",
      "L4_SRC_PORT                   39180.0    47872.5    55307.0      65535.0   \n",
      "L4_DST_PORT                      80.0     1864.0     8009.0      65535.0   \n",
      "PROTOCOL                          6.0        6.0        6.0         17.0   \n",
      "L7_PROTO                          0.0        0.0        7.0        244.0   \n",
      "IN_BYTES                         44.0       44.0      112.0  228223480.0   \n",
      "OUT_BYTES                        40.0       40.0       40.0  243219688.0   \n",
      "IN_PKTS                           1.0        1.0        2.0      37817.0   \n",
      "OUT_PKTS                          1.0        1.0        1.0      61110.0   \n",
      "TCP_FLAGS                        22.0       22.0       22.0        214.0   \n",
      "FLOW_DURATION_MILLISECONDS  4277976.0  4294966.0  4294967.0    4294967.0   \n",
      "\n",
      "                              Skewness       Kurtosis  \n",
      "L4_SRC_PORT                  -1.319698       3.155692  \n",
      "L4_DST_PORT                   2.381533       4.804060  \n",
      "PROTOCOL                      3.664847      12.316612  \n",
      "L7_PROTO                      5.307620      28.591330  \n",
      "IN_BYTES                    359.829745  165705.600333  \n",
      "OUT_BYTES                   254.102191   68272.001260  \n",
      "IN_PKTS                      36.610446    2022.388419  \n",
      "OUT_PKTS                    116.184327   25729.665264  \n",
      "TCP_FLAGS                     4.084008     127.362199  \n",
      "FLOW_DURATION_MILLISECONDS   -1.550312       0.437720  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT.csv\")\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "\n",
    "# Compute statistical summaries\n",
    "summary_stats = X.describe().T  # Compute mean, std, min, 25%, 50%, 75%, max\n",
    "summary_stats['Skewness'] = X.skew()  # Add skewness\n",
    "summary_stats['Kurtosis'] = X.kurt()  # Add kurtosis\n",
    "\n",
    "# Print the numerical summaries\n",
    "print(\"Feature Distribution Summary:\")\n",
    "print(summary_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e4fd45-9cf1-4e79-aed8-1f88460637cf",
   "metadata": {},
   "source": [
    "### Statistical comparison of original and drift feature using the Kolmogorov-Smirnov test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "61c96976-338e-40a3-95b9-f8da6424d83e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drift Analysis Results (Kolmogorov-Smirnov Test):\n",
      "{'IN_BYTES': {'KS Statistic': 0.49789535077487085, 'P-Value': 0.0}, 'OUT_BYTES': {'KS Statistic': 0.4988251958006999, 'P-Value': 0.0}}\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import ks_2samp\n",
    "\n",
    "# Load the dataset\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT.csv\")\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "\n",
    "# Copy the dataset to avoid altering the original\n",
    "X_drifted = X.copy()\n",
    "\n",
    "# Parameters for noise\n",
    "noise_mean = 0\n",
    "noise_std = 0.2\n",
    "\n",
    "# Introduce Gaussian noise into IN_BYTES and OUT_BYTES\n",
    "X_drifted[\"IN_BYTES\"] = X[\"IN_BYTES\"] + np.random.normal(noise_mean, noise_std * X[\"IN_BYTES\"].std(), size=len(X))\n",
    "X_drifted[\"OUT_BYTES\"] = X[\"OUT_BYTES\"] + np.random.normal(noise_mean, noise_std * X[\"OUT_BYTES\"].std(), size=len(X))\n",
    "\n",
    "# Statistical comparison using the Kolmogorov-Smirnov test\n",
    "results = {}\n",
    "for feature in [\"IN_BYTES\", \"OUT_BYTES\"]:\n",
    "    stat, p_value = ks_2samp(X[feature], X_drifted[feature])\n",
    "    results[feature] = {\"KS Statistic\": stat, \"P-Value\": p_value}\n",
    "\n",
    "print(\"Drift Analysis Results (Kolmogorov-Smirnov Test):\")\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "155680f7-2b92-444e-bfdd-3321c4abd6b6",
   "metadata": {},
   "source": [
    "## Experiment 3: Detection Accuracy Under Gradual Concept Drift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "936905a9-c326-43bb-8e1a-8653c47a3c05",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:   5%|▋            | 10054/200000 [00:27<08:33, 369.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 10000:\n",
      "ALMA - Accuracy: 0.9945, Recall: 0.9972\n",
      "ALMASWA - Accuracy: 0.9927, Recall: 0.9988\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  10%|█▎           | 20051/200000 [00:54<08:04, 371.50it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 20000:\n",
      "ALMA - Accuracy: 0.9968, Recall: 0.9984\n",
      "ALMASWA - Accuracy: 0.9807, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  15%|█▉           | 30048/200000 [01:18<06:57, 407.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 30000:\n",
      "ALMA - Accuracy: 0.9986, Recall: 0.9993\n",
      "ALMASWA - Accuracy: 0.9974, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  20%|██▌          | 40078/200000 [01:42<06:34, 405.29it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 40000:\n",
      "ALMA - Accuracy: 0.9977, Recall: 0.9988\n",
      "ALMASWA - Accuracy: 0.9969, Recall: 1.0000\n",
      "\n",
      "Drift step 1: Increasing noise_std to 0.10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  25%|███▎         | 50054/200000 [02:09<06:42, 372.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 50000:\n",
      "ALMA - Accuracy: 0.9866, Recall: 0.9872\n",
      "ALMASWA - Accuracy: 0.9968, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  30%|███▉         | 60061/200000 [02:35<06:07, 381.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 60000:\n",
      "ALMA - Accuracy: 0.9309, Recall: 0.9318\n",
      "ALMASWA - Accuracy: 0.9964, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  35%|████▌        | 70041/200000 [03:02<05:56, 364.35it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 70000:\n",
      "ALMA - Accuracy: 0.8390, Recall: 0.8396\n",
      "ALMASWA - Accuracy: 0.9972, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  40%|█████▏       | 80052/200000 [03:27<05:27, 365.80it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 80000:\n",
      "ALMA - Accuracy: 0.9697, Recall: 0.9714\n",
      "ALMASWA - Accuracy: 0.9665, Recall: 0.9919\n",
      "\n",
      "Drift step 2: Increasing noise_std to 0.20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  45%|█████▊       | 90053/200000 [03:53<05:18, 345.72it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 90000:\n",
      "ALMA - Accuracy: 0.9683, Recall: 0.9756\n",
      "ALMASWA - Accuracy: 0.9722, Recall: 0.9914\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  50%|██████      | 100061/200000 [04:19<04:40, 356.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 100000:\n",
      "ALMA - Accuracy: 0.9718, Recall: 0.9740\n",
      "ALMASWA - Accuracy: 0.9949, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  55%|██████▌     | 110063/200000 [04:45<03:58, 376.60it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 110000:\n",
      "ALMA - Accuracy: 0.9783, Recall: 0.9861\n",
      "ALMASWA - Accuracy: 0.9779, Recall: 0.9997\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  60%|███████▏    | 120064/200000 [05:11<03:37, 366.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 120000:\n",
      "ALMA - Accuracy: 0.9646, Recall: 0.9687\n",
      "ALMASWA - Accuracy: 0.9925, Recall: 1.0000\n",
      "\n",
      "Drift step 3: Increasing noise_std to 0.30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  65%|███████▊    | 130062/200000 [05:36<03:04, 378.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 130000:\n",
      "ALMA - Accuracy: 0.9869, Recall: 0.9926\n",
      "ALMASWA - Accuracy: 0.9836, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  70%|████████▍   | 140045/200000 [06:02<02:33, 389.67it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 140000:\n",
      "ALMA - Accuracy: 0.9558, Recall: 0.9573\n",
      "ALMASWA - Accuracy: 0.9964, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  75%|█████████   | 150081/200000 [06:56<02:07, 392.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 150000:\n",
      "ALMA - Accuracy: 0.9745, Recall: 0.9829\n",
      "ALMASWA - Accuracy: 0.9800, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  80%|█████████▌  | 160052/200000 [07:20<01:38, 405.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 160000:\n",
      "ALMA - Accuracy: 0.9740, Recall: 0.9772\n",
      "ALMASWA - Accuracy: 0.9945, Recall: 1.0000\n",
      "\n",
      "Drift step 4: Increasing noise_std to 0.40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  85%|██████████▏ | 170048/200000 [24:31<01:12, 412.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 170000:\n",
      "ALMA - Accuracy: 0.9709, Recall: 0.9777\n",
      "ALMASWA - Accuracy: 0.9788, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  90%|██████████▊ | 180079/200000 [24:54<00:47, 415.08it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 180000:\n",
      "ALMA - Accuracy: 0.9757, Recall: 0.9790\n",
      "ALMASWA - Accuracy: 0.9945, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  95%|███████████▍| 190047/200000 [25:19<00:25, 392.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 190000:\n",
      "ALMA - Accuracy: 0.9798, Recall: 0.9834\n",
      "ALMASWA - Accuracy: 0.9699, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream: 100%|████████████| 200000/200000 [25:43<00:00, 129.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 200000:\n",
      "ALMA - Accuracy: 0.9641, Recall: 0.9709\n",
      "ALMASWA - Accuracy: 0.9875, Recall: 1.0000\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from collections import deque\n",
    "from river import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the dataset\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT.csv\")\n",
    "\n",
    "# Define feature columns and target\n",
    "feature_columns = [col for col in data.columns if col not in ['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label']]\n",
    "X = data[feature_columns]\n",
    "y = data['Label'] \n",
    "\n",
    "# Parameters for drift simulation\n",
    "total_instances = 200000\n",
    "drift_steps = 5\n",
    "instances_per_drift = total_instances // drift_steps\n",
    "initial_noise_std = 0.0\n",
    "noise_increment = 0.10\n",
    "\n",
    "def gradual_drift_stream(X, y, total_instances, drift_steps, instances_per_drift, \n",
    "                         noise_increment, feature_cols, target_col):\n",
    "    \"\"\"\n",
    "    Generator that yields data instances with gradual concept drift.\n",
    "    Noise is incrementally added to specified features every drift step.\n",
    "    \"\"\"\n",
    "    current_noise_std = initial_noise_std\n",
    "    for i in tqdm(range(total_instances), desc=\"Creating data stream\"):\n",
    "        if i > 0 and i % instances_per_drift == 0:\n",
    "            current_noise_std += noise_increment\n",
    "            print(f\"\\nDrift step {i // instances_per_drift}: Increasing noise_std to {current_noise_std:.2f}\")\n",
    "        \n",
    "        # Apply noise to specified features\n",
    "        instance = X.iloc[i].copy()\n",
    "        instance[\"IN_BYTES\"] += np.random.normal(0, current_noise_std * X[\"IN_BYTES\"].std())\n",
    "        instance[\"OUT_BYTES\"] += np.random.normal(0, current_noise_std * X[\"OUT_BYTES\"].std())\n",
    "        \n",
    "        label = y.iloc[i]\n",
    "        yield instance.to_dict(), label\n",
    "\n",
    "# Initialize classifiers\n",
    "alma = linear_model.ALMAClassifier()\n",
    "almaswa = ALMASWAClassifier()\n",
    "\n",
    "# Metrics tracking\n",
    "accuracy_alma = []\n",
    "accuracy_almaswa = []\n",
    "recall_alma = []\n",
    "recall_almaswa = []\n",
    "evaluation_points = []\n",
    "\n",
    "window_size = 10000  # Sliding window size\n",
    "slide_interval = 10000  # Evaluate every 10,000 instances\n",
    "\n",
    "# Queues to store true labels and predictions\n",
    "true_labels = deque(maxlen=window_size)\n",
    "predictions_alma = deque(maxlen=window_size)\n",
    "predictions_almaswa = deque(maxlen=window_size)\n",
    "\n",
    "# Create the data stream\n",
    "data_stream = gradual_drift_stream(\n",
    "    X=X,\n",
    "    y=y,\n",
    "    total_instances=total_instances,\n",
    "    drift_steps=drift_steps,\n",
    "    instances_per_drift=instances_per_drift,\n",
    "    noise_increment=noise_increment,\n",
    "    feature_cols=feature_columns,\n",
    "    target_col='Label'\n",
    ")\n",
    "\n",
    "# Iterate through the data stream\n",
    "for i, (x, y_true) in enumerate(data_stream):\n",
    "    # Make predictions before learning\n",
    "    y_pred_alma = alma.predict_one(x)\n",
    "    y_pred_almaswa = almaswa.predict_one(x)\n",
    "\n",
    "    # Update the queues with true labels and predictions\n",
    "    true_labels.append(y_true)\n",
    "    predictions_alma.append(y_pred_alma)\n",
    "    predictions_almaswa.append(y_pred_almaswa)\n",
    "\n",
    "    # Learn from the current instance\n",
    "    alma.learn_one(x, y_true)\n",
    "    almaswa.learn_one(x, y_true)\n",
    "\n",
    "    # Evaluate at each sliding window\n",
    "    if (i + 1) % slide_interval == 0:\n",
    "        # Initialize River's metrics\n",
    "        acc_metric_alma = metrics.Accuracy()\n",
    "        acc_metric_almaswa = metrics.Accuracy()\n",
    "        rec_metric_alma = metrics.Recall()\n",
    "        rec_metric_almaswa = metrics.Recall()\n",
    "\n",
    "        # Update metrics for ALMA Classifier\n",
    "        for true, pred in zip(true_labels, predictions_alma):\n",
    "            acc_metric_alma.update(true, pred)\n",
    "            rec_metric_alma.update(true, pred)\n",
    "        \n",
    "        # Update metrics for ALMASWA Classifier\n",
    "        for true, pred in zip(true_labels, predictions_almaswa):\n",
    "            acc_metric_almaswa.update(true, pred)\n",
    "            rec_metric_almaswa.update(true, pred)\n",
    "\n",
    "        # Append metrics\n",
    "        evaluation_points.append(i + 1)\n",
    "        accuracy_alma.append(acc_metric_alma.get())\n",
    "        accuracy_almaswa.append(acc_metric_almaswa.get())\n",
    "        recall_alma.append(rec_metric_alma.get())\n",
    "        recall_almaswa.append(rec_metric_almaswa.get())\n",
    "\n",
    "        # Print metrics\n",
    "        print(f\"\\nEvaluation at instance {i + 1}:\")\n",
    "        print(f\"ALMA - Accuracy: {acc_metric_alma.get():.4f}, Recall: {rec_metric_alma.get():.4f}\")\n",
    "        print(f\"ALMASWA - Accuracy: {acc_metric_almaswa.get():.4f}, Recall: {rec_metric_almaswa.get():.4f}\")\n",
    "\n",
    "# Define the plotting function\n",
    "def plot_metrics(evaluation_points, accuracy_alma, accuracy_almaswa, \n",
    "                rec_alma, rec_almaswa):\n",
    "    plt.figure(figsize=(18, 6))\n",
    "\n",
    "    # Plot Detection Accuracy\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(evaluation_points, accuracy_alma, label='ALMA Classifier', marker='o')\n",
    "    plt.plot(evaluation_points, accuracy_almaswa, label='ALMASWA Classifier', marker='x')\n",
    "    plt.xlabel('Number of Instances')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.title('Accuracy Over Time')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "\n",
    "    # Plot F1-Score\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(evaluation_points, recall_alma, label='ALMA Classifier', marker='o')\n",
    "    plt.plot(evaluation_points, recall_almaswa, label='ALMASWA Classifier', marker='x')\n",
    "    plt.xlabel('Number of Instances')\n",
    "    plt.ylabel('Recall')\n",
    "    plt.title('Recall Over Time')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('/Users/promisea/SAMKNN/ALMASWA/BoT_Gradual_Drift.png')\n",
    "    plt.show()\n",
    "\n",
    "# Plot the metrics\n",
    "plot_metrics(evaluation_points, accuracy_alma, accuracy_almaswa, recall_alma, recall_almaswa)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dc8b2f4-ca13-4dbd-9c79-7d015229a284",
   "metadata": {},
   "source": [
    "# Experiment 4: Response to Sudden Concept Drift Events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1ee5a07b-717a-403e-8129-23341fd59be4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:   5%|▋            | 10063/200000 [00:23<07:08, 443.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 10000:\n",
      "ALMA - Accuracy: 0.9945, Recall: 0.9972\n",
      "ALMASWA - Accuracy: 0.9927, Recall: 0.9963\n",
      "\n",
      "Baseline Accuracy (ALMA): 0.9945\n",
      "Baseline Recall (ALMA): 0.9972\n",
      "Baseline Accuracy (ALMASWA): 0.9927\n",
      "Baseline Recall (ALMASWA): 0.9963\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  10%|█▎           | 20079/200000 [00:45<06:47, 441.88it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 20000:\n",
      "ALMA - Accuracy: 0.9968, Recall: 0.9984\n",
      "ALMASWA - Accuracy: 0.9807, Recall: 0.9903\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  15%|█▉           | 30088/200000 [01:08<06:30, 435.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 30000:\n",
      "ALMA - Accuracy: 0.9986, Recall: 0.9993\n",
      "ALMASWA - Accuracy: 0.9974, Recall: 0.9987\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  20%|██▌          | 40083/200000 [01:30<06:18, 422.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 40000:\n",
      "ALMA - Accuracy: 0.9977, Recall: 0.9988\n",
      "ALMASWA - Accuracy: 0.9969, Recall: 0.9984\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  25%|███▎         | 50052/200000 [01:53<05:47, 431.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 50000:\n",
      "ALMA - Accuracy: 0.9988, Recall: 0.9994\n",
      "ALMASWA - Accuracy: 0.9968, Recall: 0.9984\n",
      "ALMA recovered at instance 50000\n",
      "ALMASWA recovered at instance 50000\n",
      "\n",
      "Sudden drift introduced at instance 50000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  30%|███▉         | 60063/200000 [02:16<05:38, 412.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 60000:\n",
      "ALMA - Accuracy: 0.8931, Recall: 0.9434\n",
      "ALMASWA - Accuracy: 0.9898, Recall: 0.9949\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  35%|████▌        | 70083/200000 [02:39<05:12, 415.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 70000:\n",
      "ALMA - Accuracy: 0.7843, Recall: 0.8789\n",
      "ALMASWA - Accuracy: 0.8738, Recall: 0.9327\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  40%|█████▏       | 80087/200000 [03:01<04:26, 449.88it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 80000:\n",
      "ALMA - Accuracy: 0.9625, Recall: 0.9804\n",
      "ALMASWA - Accuracy: 0.9585, Recall: 0.9788\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  45%|█████▊       | 90052/200000 [03:23<04:12, 436.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 90000:\n",
      "ALMA - Accuracy: 0.9675, Recall: 0.9833\n",
      "ALMASWA - Accuracy: 0.9700, Recall: 0.9848\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  50%|██████      | 100092/200000 [03:45<03:42, 449.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 100000:\n",
      "ALMA - Accuracy: 0.9675, Recall: 0.9834\n",
      "ALMASWA - Accuracy: 0.9865, Recall: 0.9932\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  55%|██████▌     | 110089/200000 [04:07<03:22, 444.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 110000:\n",
      "ALMA - Accuracy: 0.9780, Recall: 0.9887\n",
      "ALMASWA - Accuracy: 0.9744, Recall: 0.9870\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  60%|███████▏    | 120057/200000 [04:29<03:16, 406.58it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 120000:\n",
      "ALMA - Accuracy: 0.9590, Recall: 0.9790\n",
      "ALMASWA - Accuracy: 0.9843, Recall: 0.9921\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  65%|███████▊    | 130069/200000 [04:51<02:36, 447.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 130000:\n",
      "ALMA - Accuracy: 0.9877, Recall: 0.9937\n",
      "ALMASWA - Accuracy: 0.9836, Recall: 0.9917\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  70%|████████▍   | 140080/200000 [05:13<02:14, 445.80it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 140000:\n",
      "ALMA - Accuracy: 0.9531, Recall: 0.9759\n",
      "ALMASWA - Accuracy: 0.9957, Recall: 0.9978\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  75%|█████████   | 150094/200000 [05:35<01:50, 453.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 150000:\n",
      "ALMA - Accuracy: 0.9741, Recall: 0.9867\n",
      "ALMASWA - Accuracy: 0.9781, Recall: 0.9889\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  80%|█████████▌  | 160050/200000 [05:56<01:28, 448.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 160000:\n",
      "ALMA - Accuracy: 0.9718, Recall: 0.9857\n",
      "ALMASWA - Accuracy: 0.9905, Recall: 0.9952\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  85%|██████████▏ | 170056/200000 [06:18<01:07, 443.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 170000:\n",
      "ALMA - Accuracy: 0.9702, Recall: 0.9847\n",
      "ALMASWA - Accuracy: 0.9775, Recall: 0.9886\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  90%|██████████▊ | 180054/200000 [06:40<00:44, 449.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 180000:\n",
      "ALMA - Accuracy: 0.9752, Recall: 0.9874\n",
      "ALMASWA - Accuracy: 0.9929, Recall: 0.9964\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  95%|███████████▍| 190078/200000 [07:01<00:22, 450.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 190000:\n",
      "ALMA - Accuracy: 0.9803, Recall: 0.9898\n",
      "ALMASWA - Accuracy: 0.9690, Recall: 0.9843\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream: 100%|████████████| 200000/200000 [07:23<00:00, 450.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 200000:\n",
      "ALMA - Accuracy: 0.9658, Recall: 0.9825\n",
      "ALMASWA - Accuracy: 0.9828, Recall: 0.9913\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from collections import deque\n",
    "from river import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ------------------------------\n",
    "# Load and Prepare the Dataset\n",
    "# ------------------------------\n",
    "\n",
    "# Load the dataset\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT.csv\")\n",
    "\n",
    "# Define feature columns and target\n",
    "feature_columns = [col for col in data.columns if col not in ['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label']]\n",
    "X = data[feature_columns]\n",
    "y = data['Label']  # Assuming 'Label' is the target variable\n",
    "\n",
    "# ------------------------------\n",
    "# Parameters for Drift Simulation\n",
    "# ------------------------------\n",
    "\n",
    "total_instances = 200000\n",
    "drift_point = 50000  # Instance at which sudden drift occurs\n",
    "noise_before_drift = 0.0\n",
    "noise_after_drift = 0.8  # Increased noise to simulate sudden drift\n",
    "noise_increment = 0.0  # Not used in sudden drift\n",
    "\n",
    "# ------------------------------\n",
    "# Data Stream Generator with Sudden Drift\n",
    "# ------------------------------\n",
    "\n",
    "def sudden_drift_stream(X, y, total_instances, drift_point, \n",
    "                        noise_before, noise_after, feature_cols, target_col):\n",
    "    \"\"\"\n",
    "    Generator that yields data instances with a sudden concept drift.\n",
    "    Noise is added to specified features at the drift point.\n",
    "    \"\"\"\n",
    "    for i in tqdm(range(total_instances), desc=\"Creating data stream\"):\n",
    "        if i == drift_point:\n",
    "            print(f\"\\nSudden drift introduced at instance {i}\")\n",
    "            current_noise_std = noise_after\n",
    "        elif i < drift_point:\n",
    "            current_noise_std = noise_before\n",
    "        # After drift point, use increased noise\n",
    "        else:\n",
    "            current_noise_std = noise_after\n",
    "        \n",
    "        # Apply noise to specified features\n",
    "        instance = X.iloc[i].copy()\n",
    "        instance[\"IN_BYTES\"] += np.random.normal(0, current_noise_std * X[\"IN_BYTES\"].std())\n",
    "        instance[\"OUT_BYTES\"] += np.random.normal(0, current_noise_std * X[\"OUT_BYTES\"].std())\n",
    "        \n",
    "        label = y.iloc[i]\n",
    "        yield instance.to_dict(), label\n",
    "\n",
    "# Initialize classifiers\n",
    "alma = linear_model.ALMAClassifier()\n",
    "almaswa = ALMASWAClassifier()\n",
    "\n",
    "# ------------------------------\n",
    "# Metrics Tracking Initialization\n",
    "# ------------------------------\n",
    "\n",
    "accuracy_alma = []\n",
    "accuracy_almaswa = []\n",
    "recall_alma = []\n",
    "recall_almaswa = []\n",
    "evaluation_points = []\n",
    "\n",
    "window_size = 10000  # Sliding window size\n",
    "slide_interval = 10000  # Evaluate every 10,000 instances\n",
    "\n",
    "# Queues to store true labels and predictions\n",
    "true_labels = deque(maxlen=window_size)\n",
    "predictions_alma = deque(maxlen=window_size)\n",
    "predictions_almaswa = deque(maxlen=window_size)\n",
    "\n",
    "# Detection Delay Tracking\n",
    "# Define baseline metrics before drift\n",
    "baseline_accuracy = None\n",
    "baseline_recall = None\n",
    "\n",
    "# Threshold to consider recovery (95% of baseline)\n",
    "recovery_threshold = 0.95\n",
    "\n",
    "# Flags and counters\n",
    "alma_recovered = False\n",
    "almaswa_recovered = False\n",
    "alma_recovery_point = None\n",
    "almaswa_recovery_point = None\n",
    "\n",
    "# ------------------------------\n",
    "# Create the Data Stream\n",
    "# ------------------------------\n",
    "\n",
    "data_stream = sudden_drift_stream(\n",
    "    X=X,\n",
    "    y=y,\n",
    "    total_instances=total_instances,\n",
    "    drift_point=drift_point,\n",
    "    noise_before=noise_before_drift,\n",
    "    noise_after=noise_after_drift,\n",
    "    feature_cols=feature_columns,\n",
    "    target_col='Label'\n",
    ")\n",
    "\n",
    "# ------------------------------\n",
    "# Iterate Through the Data Stream\n",
    "# ------------------------------\n",
    "\n",
    "for i, (x, y_true) in enumerate(data_stream):\n",
    "    # Make predictions before learning\n",
    "    y_pred_alma = alma.predict_one(x)\n",
    "    y_pred_almaswa = almaswa.predict_one(x)\n",
    "\n",
    "    # Update the queues with true labels and predictions\n",
    "    true_labels.append(y_true)\n",
    "    predictions_alma.append(y_pred_alma)\n",
    "    predictions_almaswa.append(y_pred_almaswa)\n",
    "\n",
    "    # Learn from the current instance\n",
    "    alma.learn_one(x, y_true)\n",
    "    almaswa.learn_one(x, y_true)\n",
    "\n",
    "    # Evaluate at each sliding window\n",
    "    if (i + 1) % slide_interval == 0:\n",
    "        # Initialize River's metrics\n",
    "        acc_metric_alma = metrics.Accuracy()\n",
    "        acc_metric_almaswa = metrics.Accuracy()\n",
    "        rec_metric_alma = metrics.F1()\n",
    "        rec_metric_almaswa = metrics.F1()\n",
    "\n",
    "        # Update metrics for ALMA Classifier\n",
    "        for true, pred in zip(true_labels, predictions_alma):\n",
    "            acc_metric_alma.update(true, pred)\n",
    "            rec_metric_alma.update(true, pred)\n",
    "        \n",
    "        # Update metrics for ALMASWA Classifier\n",
    "        for true, pred in zip(true_labels, predictions_almaswa):\n",
    "            acc_metric_almaswa.update(true, pred)\n",
    "            rec_metric_almaswa.update(true, pred)\n",
    "\n",
    "        # Append metrics\n",
    "        evaluation_points.append(i + 1)\n",
    "        accuracy_alma.append(acc_metric_alma.get())\n",
    "        accuracy_almaswa.append(acc_metric_almaswa.get())\n",
    "        recall_alma.append(rec_metric_alma.get())\n",
    "        recall_almaswa.append(rec_metric_almaswa.get())\n",
    "\n",
    "        # Print metrics\n",
    "        print(f\"\\nEvaluation at instance {i + 1}:\")\n",
    "        print(f\"ALMA - Accuracy: {acc_metric_alma.get():.4f}, Recall: {rec_metric_alma.get():.4f}\")\n",
    "        print(f\"ALMASWA - Accuracy: {acc_metric_almaswa.get():.4f}, Recall: {rec_metric_almaswa.get():.4f}\")\n",
    "\n",
    "        # Set baseline metrics before drift (first window before drift)\n",
    "        if i + 1 == window_size and i + 1 < drift_point:\n",
    "            baseline_accuracy = acc_metric_alma.get()\n",
    "            baseline_recall = rec_metric_alma.get()\n",
    "            print(f\"\\nBaseline Accuracy (ALMA): {baseline_accuracy:.4f}\")\n",
    "            print(f\"Baseline Recall (ALMA): {baseline_recall:.4f}\")\n",
    "            baseline_accuracy_almaswa = acc_metric_almaswa.get()\n",
    "            baseline_recall_almaswa = rec_metric_almaswa.get()\n",
    "            print(f\"Baseline Accuracy (ALMASWA): {baseline_accuracy_almaswa:.4f}\")\n",
    "            print(f\"Baseline Recall (ALMASWA): {baseline_recall_almaswa:.4f}\")\n",
    "\n",
    "        # After drift, check for recovery\n",
    "        if i + 1 >= drift_point:\n",
    "            # ALMA Recovery Check\n",
    "            if not alma_recovered and baseline_accuracy is not None:\n",
    "                if acc_metric_alma.get() >= recovery_threshold * baseline_accuracy:\n",
    "                    alma_recovered = True\n",
    "                    alma_recovery_point = i + 1\n",
    "                    print(f\"ALMA recovered at instance {i + 1}\")\n",
    "            \n",
    "            # ALMASWA Recovery Check\n",
    "            if not almaswa_recovered and baseline_accuracy_almaswa is not None:\n",
    "                if acc_metric_almaswa.get() >= recovery_threshold * baseline_accuracy_almaswa:\n",
    "                    almaswa_recovered = True\n",
    "                    almaswa_recovery_point = i + 1\n",
    "                    print(f\"ALMASWA recovered at instance {i + 1}\")\n",
    "\n",
    "# ------------------------------\n",
    "# Define the Plotting Function\n",
    "# ------------------------------\n",
    "\n",
    "def plot_metrics(evaluation_points, accuracy_alma, accuracy_almaswa, \n",
    "                recall_alma, recall_almaswa, drift_point, alma_recovery, almaswa_recovery):\n",
    "    plt.figure(figsize=(18, 6))\n",
    "\n",
    "    # Plot Detection Accuracy\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(evaluation_points, accuracy_alma, label='ALMA Classifier', marker='o')\n",
    "    plt.plot(evaluation_points, accuracy_almaswa, label='ALMASWA Classifier', marker='x')\n",
    "    plt.axvline(x=drift_point, color='r', linestyle='--', label='Drift Point')\n",
    "    # if alma_recovery:\n",
    "    #     plt.axvline(x=alma_recovery, color='g', linestyle=':', label='ALMA Recovery')\n",
    "    # if almaswa_recovery:\n",
    "    #     plt.axvline(x=almaswa_recovery, color='b', linestyle=':', label='ALMASWA Recovery')\n",
    "    plt.xlabel('Number of Instances')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.title('Accuracy Over Time')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "\n",
    "    # Plot F1-Score\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(evaluation_points, recall_alma, label='ALMA Classifier', marker='o')\n",
    "    plt.plot(evaluation_points, recall_almaswa, label='ALMASWA Classifier', marker='x')\n",
    "    plt.axvline(x=drift_point, color='r', linestyle='--', label='Drift Point')\n",
    "    plt.xlabel('Number of Instances')\n",
    "    plt.ylabel('Recall')\n",
    "    plt.title('Recall Over Time')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('/Users/promisea/SAMKNN/ALMASWA/BoT_Sudden_Drift.png')\n",
    "    plt.show()\n",
    "\n",
    "# ------------------------------\n",
    "# Plot the Metrics\n",
    "# ------------------------------\n",
    "\n",
    "plot_metrics(\n",
    "    evaluation_points, \n",
    "    accuracy_alma, \n",
    "    accuracy_almaswa, \n",
    "    recall_alma, \n",
    "    recall_almaswa, \n",
    "    drift_point=drift_point, \n",
    "    alma_recovery=alma_recovery_point, \n",
    "    almaswa_recovery=almaswa_recovery_point\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adac7f7a-727a-4558-822c-c006e6a5e227",
   "metadata": {},
   "source": [
    "## Experiment 5: Paired t-test using different seeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "39d7a97a-7a6e-466a-94fd-7b4b3ed59215",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running experiment with seed=0 ...\n",
      "  ALMA accuracy: 0.9544\n",
      "  ALMA+SWA accuracy: 0.9769\n",
      "\n",
      "Running experiment with seed=1 ...\n",
      "  ALMA accuracy: 0.9543\n",
      "  ALMA+SWA accuracy: 0.9769\n",
      "\n",
      "Running experiment with seed=2 ...\n",
      "  ALMA accuracy: 0.9543\n",
      "  ALMA+SWA accuracy: 0.9769\n",
      "\n",
      "Running experiment with seed=3 ...\n",
      "  ALMA accuracy: 0.9543\n",
      "  ALMA+SWA accuracy: 0.9769\n",
      "\n",
      "Running experiment with seed=4 ...\n",
      "  ALMA accuracy: 0.9542\n",
      "  ALMA+SWA accuracy: 0.9769\n",
      "\n",
      "=== Summary of 10 Runs ===\n",
      "ALMA Accuracies   : [0.9543676053991001, 0.9542726212297951, 0.9542976170638227, 0.9542659556740544, 0.954169305115814]\n",
      "ALMASWA Accuracies: [0.9768971838026995, 0.9768955174137643, 0.9768971838026995, 0.9768855190801533, 0.9769005165805699]\n",
      "\n",
      "Mean ± Std for ALMA      : 0.9543 ± 0.0001\n",
      "Mean ± Std for ALMA+SWA : 0.9769 ± 0.0000\n",
      "\n",
      "Paired t-test (ALMA vs ALMA+SWA): t_stat=-698.6088, p-value=0.000000\n",
      "=> Statistically significant difference at alpha=0.05\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT.csv\")\n",
    "\n",
    "# Separate features (X) and label (y)\n",
    "X_full = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y_full = data['Label']\n",
    "\n",
    "# 2. We'll run 10 experiments, each with a different random seed\n",
    "NUM_RUNS = 5\n",
    "seeds = range(NUM_RUNS)  # e.g., 0 through 9\n",
    "\n",
    "# We'll store final accuracy for each run\n",
    "alma_accuracies = []\n",
    "almaswa_accuracies = []\n",
    "\n",
    "for seed in seeds:\n",
    "    print(f\"Running experiment with seed={seed} ...\")\n",
    "    \n",
    "    # 2a. Shuffle data with this seed\n",
    "    X, y = shuffle(X_full, y_full, random_state=seed)\n",
    "    \n",
    "    # 2b. Convert to a River stream\n",
    "    data_stream = stream.iter_pandas(X, y)\n",
    "    \n",
    "    # 2c. Instantiate models & metrics\n",
    "    model_alma = linear_model.ALMAClassifier()\n",
    "    model_almaswa = ALMASWAClassifier()\n",
    "    \n",
    "    metric_alma = metrics.Accuracy()\n",
    "    metric_almaswa = metrics.Accuracy()\n",
    "    \n",
    "    # 2d. Progressive learning/evaluation\n",
    "    for x_dict, y_true in data_stream:\n",
    "        # ALMA\n",
    "        y_pred_alma = model_alma.predict_one(x_dict)\n",
    "        metric_alma.update(y_true, y_pred_alma)\n",
    "        model_alma.learn_one(x_dict, y_true)\n",
    "        \n",
    "        # ALMA+SWA\n",
    "        y_pred_almaswa = model_almaswa.predict_one(x_dict)\n",
    "        metric_almaswa.update(y_true, y_pred_almaswa)\n",
    "        model_almaswa.learn_one(x_dict, y_true)\n",
    "    \n",
    "    # 2e. Collect final accuracies for this run\n",
    "    alma_accuracies.append(metric_alma.get())\n",
    "    almaswa_accuracies.append(metric_almaswa.get())\n",
    "    \n",
    "    print(f\"  ALMA accuracy: {metric_alma.get():.4f}\")\n",
    "    print(f\"  ALMA+SWA accuracy: {metric_almaswa.get():.4f}\")\n",
    "    print(\"\")\n",
    "\n",
    "# 3. Print summary of results\n",
    "print(\"=== Summary of 10 Runs ===\")\n",
    "print(\"ALMA Accuracies   :\", alma_accuracies)\n",
    "print(\"ALMASWA Accuracies:\", almaswa_accuracies)\n",
    "\n",
    "print(\"\\nMean ± Std for ALMA      : \"\n",
    "      f\"{np.mean(alma_accuracies):.4f} ± {np.std(alma_accuracies):.4f}\")\n",
    "print(\"Mean ± Std for ALMA+SWA : \"\n",
    "      f\"{np.mean(almaswa_accuracies):.4f} ± {np.std(almaswa_accuracies):.4f}\")\n",
    "\n",
    "# 4. (Optional) Significance test\n",
    "#    Paired t-test over the 10 runs\n",
    "t_stat, p_val = ttest_rel(alma_accuracies, almaswa_accuracies)\n",
    "print(f\"\\nPaired t-test (ALMA vs ALMA+SWA): t_stat={t_stat:.4f}, p-value={p_val:.6f}\")\n",
    "if p_val <= 0.05:\n",
    "    print(\"=> Statistically significant difference at alpha=0.05\")\n",
    "else:\n",
    "    print(\"=> No statistically significant difference at alpha=0.05\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13bb1c67-bd03-453f-bec4-e57d0757745a",
   "metadata": {},
   "source": [
    "## Experiment 6: Measuring Resource Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "56b9b5ef-51e5-400e-8a22-1cb4108f6f15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples per class: Counter({1: 586241, 0: 13859})\n",
      "Processed 100000 samples. Throughput: 12690.47 samples/sec.\n",
      "Processed 200000 samples. Throughput: 13331.43 samples/sec.\n",
      "Processed 300000 samples. Throughput: 13545.57 samples/sec.\n",
      "Processed 400000 samples. Throughput: 13421.69 samples/sec.\n",
      "Processed 500000 samples. Throughput: 13643.73 samples/sec.\n",
      "Processed 600000 samples. Throughput: 13819.78 samples/sec.\n",
      "\n",
      "Total samples processed: 600100\n",
      "Total elapsed time: 43.42 seconds\n",
      "Throughput: 13819.24 samples/second\n",
      "\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9240\n",
      "Recall: 0.9349\n",
      "\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9821\n",
      "Recall: 0.9945\n",
      "\n",
      "--- ALMAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0243 ms\n",
      "Average CPU Usage: 1.08%\n",
      "Average Memory Usage: -0.21 KB\n",
      "Average Total Processing Time per Sample: 0.0341 ms\n",
      "\n",
      "--- ALMASWAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0221 ms\n",
      "Average CPU Usage: 1.08%\n",
      "Average Memory Usage: -0.21 KB\n",
      "Average Total Processing Time per Sample: 0.0341 ms\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from sklearn.utils import shuffle\n",
    "from river import compose, preprocessing, linear_model, metrics, stream\n",
    "import time\n",
    "import psutil\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ---------------------------\n",
    "# Step 1: Initialize Classifiers and Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()  # Ensure ALMASWAClassifier is correctly imported or defined\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'Recall': metrics.Recall()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize performance tracking dictionaries\n",
    "performance_metrics = {\n",
    "    name: {\n",
    "        'Latency': [],             # Time taken per sample (predict + learn)\n",
    "        'Total_Time': [],          # Cumulative processing time per sample\n",
    "        'CPU_Usage': [],           # CPU usage in percentage\n",
    "        'Memory_Usage': []         # Memory usage in KB (Modified from MB to KB)\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize overall tracking variables\n",
    "total_samples = 0\n",
    "start_time = time.time()\n",
    "\n",
    "# Initialize psutil process for monitoring\n",
    "process = psutil.Process()\n",
    "\n",
    "# ---------------------------\n",
    "# Step 2: Load and Prepare Data\n",
    "# ---------------------------\n",
    "\n",
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT.csv\")\n",
    "print('Number of samples per class:', Counter(data['Label']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# ---------------------------\n",
    "# Step 3: Training Loop with Performance Measurement\n",
    "# ---------------------------\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    total_samples += 1\n",
    "    sample_start_time = time.time()\n",
    "\n",
    "    # Get current CPU and memory usage before processing\n",
    "    cpu_before = process.cpu_percent(interval=None)\n",
    "    memory_before = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    for name, model in classifiers.items():\n",
    "        clf_start_time = time.time()\n",
    "\n",
    "        # Predict\n",
    "        y_pred = model.predict_one(x)\n",
    "\n",
    "        # Learn\n",
    "        model.learn_one(x, true_y)\n",
    "\n",
    "        # Calculate latency for this classifier\n",
    "        clf_latency = time.time() - clf_start_time\n",
    "\n",
    "        # Update metrics\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['Recall'].update(true_y, y_pred)\n",
    "\n",
    "        # Record latency\n",
    "        performance_metrics[name]['Latency'].append(clf_latency)\n",
    "\n",
    "    # Get current CPU and memory usage after processing\n",
    "    cpu_after = process.cpu_percent(interval=None)\n",
    "    memory_after = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    # Calculate CPU and memory usage during processing\n",
    "    cpu_usage = cpu_after - cpu_before\n",
    "    memory_usage = memory_after - memory_before\n",
    "\n",
    "    # Record CPU and memory usage for each classifier (approximate)\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['CPU_Usage'].append(cpu_usage / len(classifiers))\n",
    "        performance_metrics[name]['Memory_Usage'].append(memory_usage / len(classifiers))\n",
    "\n",
    "    # Calculate total processing time for this sample\n",
    "    total_time = time.time() - sample_start_time\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['Total_Time'].append(total_time / len(classifiers))\n",
    "\n",
    "    # Optional: Print progress every 100,000 samples\n",
    "    if (i + 1) % 100000 == 0:\n",
    "        elapsed_time = time.time() - start_time\n",
    "        throughput = total_samples / elapsed_time\n",
    "        print(f'Processed {i + 1} samples. Throughput: {throughput:.2f} samples/sec.')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 4: Calculate Throughput\n",
    "# ---------------------------\n",
    "\n",
    "# Calculate total elapsed time\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time - start_time\n",
    "\n",
    "# Calculate throughput (samples per second)\n",
    "throughput = total_samples / elapsed_time\n",
    "print(f'\\nTotal samples processed: {total_samples}')\n",
    "print(f'Total elapsed time: {elapsed_time:.2f} seconds')\n",
    "print(f'Throughput: {throughput:.2f} samples/second\\n')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 5: Display Results\n",
    "# ---------------------------\n",
    "\n",
    "# Display classification metrics\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"Recall: {metric['Recall'].get():.4f}\\n\")\n",
    "\n",
    "# Display performance metrics summary\n",
    "for name, metrics_perf in performance_metrics.items():\n",
    "    avg_latency = sum(metrics_perf['Latency']) / len(metrics_perf['Latency']) if metrics_perf['Latency'] else 0\n",
    "    avg_cpu = sum(metrics_perf['CPU_Usage']) / len(metrics_perf['CPU_Usage']) if metrics_perf['CPU_Usage'] else 0\n",
    "    avg_memory = sum(metrics_perf['Memory_Usage']) / len(metrics_perf['Memory_Usage']) if metrics_perf['Memory_Usage'] else 0\n",
    "    avg_total_time = sum(metrics_perf['Total_Time']) / len(metrics_perf['Total_Time']) if metrics_perf['Total_Time'] else 0\n",
    "\n",
    "    print(f'--- {name} Performance ---')\n",
    "    print(f\"Average Latency per Sample: {avg_latency * 1000:.4f} ms\")\n",
    "    print(f\"Average CPU Usage: {avg_cpu:.2f}%\")\n",
    "    print(f\"Average Memory Usage: {avg_memory:.2f} KB\")  # Modified to KB\n",
    "    print(f\"Average Total Processing Time per Sample: {avg_total_time * 1000:.4f} ms\\n\")\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Enhanced Plotting Function to handle multiple classifiers side by side\n",
    "def plot_performance_side_by_side(performance_metrics, classifier_names, window_size=50000):\n",
    "    metrics_names = ['Latency']\n",
    "    num_classifiers = len(classifier_names)\n",
    "    \n",
    "    # Create subplots arranged side by side with shared y-axis\n",
    "    fig, axes = plt.subplots(1, num_classifiers, figsize=(15, 6), sharey=True)\n",
    "    \n",
    "    # If there's only one classifier, axes might not be a list\n",
    "    if num_classifiers == 1:\n",
    "        axes = [axes]\n",
    "    \n",
    "    for ax, classifier_name in zip(axes, classifier_names):\n",
    "        for metric in metrics_names:\n",
    "            metric_series = pd.Series(performance_metrics[classifier_name][metric])\n",
    "            rolling_metric = metric_series.rolling(window=window_size).mean()\n",
    "            ax.plot(rolling_metric, label=f'{metric} (Rolling Mean, window={window_size})')\n",
    "        \n",
    "        # Set titles and labels with bold font\n",
    "        ax.set_title(f'{classifier_name} Performance', fontweight='bold', fontsize=14)\n",
    "        ax.set_xlabel('Number of Instances', fontweight='bold', fontsize=12)\n",
    "        ax.set_ylabel('Latency (ms)', fontweight='bold', fontsize=12)\n",
    "        \n",
    "        # Make ticks bold\n",
    "        ax.tick_params(axis='both', which='major', labelsize=10, width=2)\n",
    "        for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "            label.set_fontweight('bold')\n",
    "        \n",
    "        # Make legend bold using prop\n",
    "        ax.legend(prop={'weight': 'bold'})\n",
    "        ax.grid(True)\n",
    "\n",
    "    # Force y-axis tick labels to be bold on all subplots\n",
    "    for ax in axes:\n",
    "        for tick in ax.yaxis.get_ticklabels():\n",
    "            tick.set_fontweight('bold')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('/Users/promisea/SAMKNN/ALMASWA/BoT_Latency.png')\n",
    "    plt.show()\n",
    "\n",
    "# Example: Plot performance metrics for both classifiers side by side\n",
    "plot_performance_side_by_side(performance_metrics, ['ALMAClassifier', 'ALMASWAClassifier'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cf3a951-ac54-4717-be17-41e0340ca5b5",
   "metadata": {},
   "source": [
    "### CPU Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bc6bb9f7-45d3-4456-8b9a-073aafe9f963",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples per class: Counter({1: 586241, 0: 13859})\n",
      "Processed 100000 samples. Throughput: 11377.43 samples/sec.\n",
      "Processed 200000 samples. Throughput: 11844.71 samples/sec.\n",
      "Processed 300000 samples. Throughput: 12033.98 samples/sec.\n",
      "Processed 400000 samples. Throughput: 12077.98 samples/sec.\n",
      "Processed 500000 samples. Throughput: 12090.83 samples/sec.\n",
      "Processed 600000 samples. Throughput: 12068.71 samples/sec.\n",
      "\n",
      "Total samples processed: 600100\n",
      "Total elapsed time: 49.73 seconds\n",
      "Throughput: 12068.29 samples/second\n",
      "\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9240\n",
      "F1 Score: 0.9600\n",
      "ROCAUC Score: 0.6970\n",
      "\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9821\n",
      "F1 Score: 0.9909\n",
      "ROCAUC Score: 0.7273\n",
      "\n",
      "--- ALMAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0240 ms\n",
      "Average CPU Usage: 1.16%\n",
      "Average Memory Usage: -0.11 KB\n",
      "Average Total Processing Time per Sample: 0.0394 ms\n",
      "\n",
      "--- ALMASWAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0220 ms\n",
      "Average CPU Usage: 1.16%\n",
      "Average Memory Usage: -0.11 KB\n",
      "Average Total Processing Time per Sample: 0.0394 ms\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from sklearn.utils import shuffle\n",
    "from river import compose, preprocessing, linear_model, metrics, stream\n",
    "import time\n",
    "import psutil\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ---------------------------\n",
    "# Step 1: Initialize Classifiers and Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()  # Ensure ALMASWAClassifier is correctly imported or defined\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'F1': metrics.F1(),\n",
    "        'ROCAUC': metrics.ROCAUC()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize performance tracking dictionaries\n",
    "performance_metrics = {\n",
    "    name: {\n",
    "        'Latency': [],             # Time taken per sample (predict + learn)\n",
    "        'Total_Time': [],          # Cumulative processing time per sample\n",
    "        'CPU_Usage': [],           # CPU usage in percentage\n",
    "        'Memory_Usage': []         # Memory usage in KB (Modified from MB to KB)\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize overall tracking variables\n",
    "total_samples = 0\n",
    "start_time = time.time()\n",
    "\n",
    "# Initialize psutil process for monitoring\n",
    "process = psutil.Process()\n",
    "\n",
    "# ---------------------------\n",
    "# Step 2: Load and Prepare Data\n",
    "# ---------------------------\n",
    "\n",
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT.csv\")\n",
    "print('Number of samples per class:', Counter(data['Label']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# ---------------------------\n",
    "# Step 3: Training Loop with Performance Measurement\n",
    "# ---------------------------\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    total_samples += 1\n",
    "    sample_start_time = time.time()\n",
    "\n",
    "    # Get current CPU and memory usage before processing\n",
    "    cpu_before = process.cpu_percent(interval=None)\n",
    "    memory_before = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    for name, model in classifiers.items():\n",
    "        clf_start_time = time.time()\n",
    "\n",
    "        # Predict\n",
    "        y_pred = model.predict_one(x)\n",
    "\n",
    "        # Learn\n",
    "        model.learn_one(x, true_y)\n",
    "\n",
    "        # Calculate latency for this classifier\n",
    "        clf_latency = time.time() - clf_start_time\n",
    "\n",
    "        # Update metrics\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['F1'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['ROCAUC'].update(true_y, y_pred)\n",
    "\n",
    "        # Record latency\n",
    "        performance_metrics[name]['Latency'].append(clf_latency)\n",
    "\n",
    "    # Get current CPU and memory usage after processing\n",
    "    cpu_after = process.cpu_percent(interval=None)\n",
    "    memory_after = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    # Calculate CPU and memory usage during processing\n",
    "    cpu_usage = cpu_after - cpu_before\n",
    "    memory_usage = memory_after - memory_before\n",
    "\n",
    "    # Record CPU and memory usage for each classifier (approximate)\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['CPU_Usage'].append(cpu_usage / len(classifiers))\n",
    "        performance_metrics[name]['Memory_Usage'].append(memory_usage / len(classifiers))\n",
    "\n",
    "    # Calculate total processing time for this sample\n",
    "    total_time = time.time() - sample_start_time\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['Total_Time'].append(total_time / len(classifiers))\n",
    "\n",
    "    # Optional: Print progress every 100,000 samples\n",
    "    if (i + 1) % 100000 == 0:\n",
    "        elapsed_time = time.time() - start_time\n",
    "        throughput = total_samples / elapsed_time\n",
    "        print(f'Processed {i + 1} samples. Throughput: {throughput:.2f} samples/sec.')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 4: Calculate Throughput\n",
    "# ---------------------------\n",
    "\n",
    "# Calculate total elapsed time\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time - start_time\n",
    "\n",
    "# Calculate throughput (samples per second)\n",
    "throughput = total_samples / elapsed_time\n",
    "print(f'\\nTotal samples processed: {total_samples}')\n",
    "print(f'Total elapsed time: {elapsed_time:.2f} seconds')\n",
    "print(f'Throughput: {throughput:.2f} samples/second\\n')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 5: Display Results\n",
    "# ---------------------------\n",
    "\n",
    "# Display classification metrics\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"F1 Score: {metric['F1'].get():.4f}\")\n",
    "    print(f\"ROCAUC Score: {metric['ROCAUC'].get():.4f}\\n\")\n",
    "\n",
    "# Display performance metrics summary\n",
    "for name, metrics_perf in performance_metrics.items():\n",
    "    avg_latency = sum(metrics_perf['Latency']) / len(metrics_perf['Latency']) if metrics_perf['Latency'] else 0\n",
    "    avg_cpu = sum(metrics_perf['CPU_Usage']) / len(metrics_perf['CPU_Usage']) if metrics_perf['CPU_Usage'] else 0\n",
    "    avg_memory = sum(metrics_perf['Memory_Usage']) / len(metrics_perf['Memory_Usage']) if metrics_perf['Memory_Usage'] else 0\n",
    "    avg_total_time = sum(metrics_perf['Total_Time']) / len(metrics_perf['Total_Time']) if metrics_perf['Total_Time'] else 0\n",
    "\n",
    "    print(f'--- {name} Performance ---')\n",
    "    print(f\"Average Latency per Sample: {avg_latency * 1000:.4f} ms\")\n",
    "    print(f\"Average CPU Usage: {avg_cpu:.2f}%\")\n",
    "    print(f\"Average Memory Usage: {avg_memory:.2f} KB\")  # Modified to KB\n",
    "    print(f\"Average Total Processing Time per Sample: {avg_total_time * 1000:.4f} ms\\n\")\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Enhanced Plotting Function to handle multiple classifiers side by side\n",
    "def plot_performance_side_by_side(performance_metrics, classifier_names, window_size=50000):\n",
    "    metrics_names = ['CPU_Usage']\n",
    "    num_classifiers = len(classifier_names)\n",
    "    \n",
    "    # Create subplots arranged side by side with shared y-axis\n",
    "    fig, axes = plt.subplots(1, num_classifiers, figsize=(15, 6), sharey=True)\n",
    "    \n",
    "    # If there's only one classifier, axes might not be a list\n",
    "    if num_classifiers == 1:\n",
    "        axes = [axes]\n",
    "    \n",
    "    for ax, classifier_name in zip(axes, classifier_names):\n",
    "        for metric in metrics_names:\n",
    "            metric_series = pd.Series(performance_metrics[classifier_name][metric])\n",
    "            rolling_metric = metric_series.rolling(window=window_size).mean()\n",
    "            ax.plot(rolling_metric, label=f'{metric} (Rolling Mean, window={window_size})')\n",
    "        \n",
    "        # Set titles and labels with bold font\n",
    "        ax.set_title(f'{classifier_name} Performance', fontweight='bold', fontsize=14)\n",
    "        ax.set_xlabel('Number of Instances', fontweight='bold', fontsize=12)\n",
    "        ax.set_ylabel('CPU Usage', fontweight='bold', fontsize=12)\n",
    "        \n",
    "        # Make ticks bold\n",
    "        ax.tick_params(axis='both', which='major', labelsize=10, width=2)\n",
    "        for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "            label.set_fontweight('bold')\n",
    "        \n",
    "        # Make legend bold using prop\n",
    "        ax.legend(prop={'weight': 'bold'})\n",
    "        ax.grid(True)\n",
    "\n",
    "    # Force y-axis tick labels to be bold on all subplots\n",
    "    for ax in axes:\n",
    "        for tick in ax.yaxis.get_ticklabels():\n",
    "            tick.set_fontweight('bold')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('/Users/promisea/SAMKNN/ALMASWA/BoT_CPU.png')\n",
    "    plt.show()\n",
    "\n",
    "# Example: Plot performance metrics for both classifiers side by side\n",
    "plot_performance_side_by_side(performance_metrics, ['ALMAClassifier', 'ALMASWAClassifier'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ed01946-808a-41d2-9ab5-b0344c5ad06b",
   "metadata": {},
   "source": [
    "### Memory usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f340980-5d56-479e-b34c-a86a4f384800",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples per class: Counter({1: 586241, 0: 13859})\n",
      "Processed 100000 samples. Throughput: 11335.21 samples/sec.\n",
      "Processed 200000 samples. Throughput: 11810.20 samples/sec.\n",
      "Processed 300000 samples. Throughput: 11835.20 samples/sec.\n",
      "Processed 400000 samples. Throughput: 11693.07 samples/sec.\n",
      "Processed 500000 samples. Throughput: 11790.83 samples/sec.\n",
      "Processed 600000 samples. Throughput: 11889.41 samples/sec.\n",
      "\n",
      "Total samples processed: 600100\n",
      "Total elapsed time: 50.48 seconds\n",
      "Throughput: 11889.00 samples/second\n",
      "\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9240\n",
      "F1 Score: 0.9600\n",
      "ROCAUC Score: 0.6970\n",
      "\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9821\n",
      "F1 Score: 0.9909\n",
      "ROCAUC Score: 0.7273\n",
      "\n",
      "--- ALMAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0243 ms\n",
      "Average CPU Usage: 1.37%\n",
      "Average Memory Usage: -0.25 KB\n",
      "Average Total Processing Time per Sample: 0.0400 ms\n",
      "\n",
      "--- ALMASWAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0224 ms\n",
      "Average CPU Usage: 1.37%\n",
      "Average Memory Usage: -0.25 KB\n",
      "Average Total Processing Time per Sample: 0.0400 ms\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from sklearn.utils import shuffle\n",
    "from river import compose, preprocessing, linear_model, metrics, stream\n",
    "import time\n",
    "import psutil\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ---------------------------\n",
    "# Step 1: Initialize Classifiers and Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()  # Ensure ALMASWAClassifier is correctly imported or defined\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'F1': metrics.F1(),\n",
    "        'ROCAUC': metrics.ROCAUC()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize performance tracking dictionaries\n",
    "performance_metrics = {\n",
    "    name: {\n",
    "        'Latency': [],             # Time taken per sample (predict + learn)\n",
    "        'Total_Time': [],          # Cumulative processing time per sample\n",
    "        'CPU_Usage': [],           # CPU usage in percentage\n",
    "        'Memory_Usage': []         # Memory usage in KB (Modified from MB to KB)\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize overall tracking variables\n",
    "total_samples = 0\n",
    "start_time = time.time()\n",
    "\n",
    "# Initialize psutil process for monitoring\n",
    "process = psutil.Process()\n",
    "\n",
    "# ---------------------------\n",
    "# Step 2: Load and Prepare Data\n",
    "# ---------------------------\n",
    "\n",
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT.csv\")\n",
    "print('Number of samples per class:', Counter(data['Label']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# ---------------------------\n",
    "# Step 3: Training Loop with Performance Measurement\n",
    "# ---------------------------\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    total_samples += 1\n",
    "    sample_start_time = time.time()\n",
    "\n",
    "    # Get current CPU and memory usage before processing\n",
    "    cpu_before = process.cpu_percent(interval=None)\n",
    "    memory_before = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    for name, model in classifiers.items():\n",
    "        clf_start_time = time.time()\n",
    "\n",
    "        # Predict\n",
    "        y_pred = model.predict_one(x)\n",
    "\n",
    "        # Learn\n",
    "        model.learn_one(x, true_y)\n",
    "\n",
    "        # Calculate latency for this classifier\n",
    "        clf_latency = time.time() - clf_start_time\n",
    "\n",
    "        # Update metrics\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['F1'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['ROCAUC'].update(true_y, y_pred)\n",
    "\n",
    "        # Record latency\n",
    "        performance_metrics[name]['Latency'].append(clf_latency)\n",
    "\n",
    "    # Get current CPU and memory usage after processing\n",
    "    cpu_after = process.cpu_percent(interval=None)\n",
    "    memory_after = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    # Calculate CPU and memory usage during processing\n",
    "    cpu_usage = cpu_after - cpu_before\n",
    "    memory_usage = memory_after - memory_before\n",
    "\n",
    "    # Record CPU and memory usage for each classifier (approximate)\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['CPU_Usage'].append(cpu_usage / len(classifiers))\n",
    "        performance_metrics[name]['Memory_Usage'].append(memory_usage / len(classifiers))\n",
    "\n",
    "    # Calculate total processing time for this sample\n",
    "    total_time = time.time() - sample_start_time\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['Total_Time'].append(total_time / len(classifiers))\n",
    "\n",
    "    # Optional: Print progress every 100,000 samples\n",
    "    if (i + 1) % 100000 == 0:\n",
    "        elapsed_time = time.time() - start_time\n",
    "        throughput = total_samples / elapsed_time\n",
    "        print(f'Processed {i + 1} samples. Throughput: {throughput:.2f} samples/sec.')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 4: Calculate Throughput\n",
    "# ---------------------------\n",
    "\n",
    "# Calculate total elapsed time\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time - start_time\n",
    "\n",
    "# Calculate throughput (samples per second)\n",
    "throughput = total_samples / elapsed_time\n",
    "print(f'\\nTotal samples processed: {total_samples}')\n",
    "print(f'Total elapsed time: {elapsed_time:.2f} seconds')\n",
    "print(f'Throughput: {throughput:.2f} samples/second\\n')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 5: Display Results\n",
    "# ---------------------------\n",
    "\n",
    "# Display classification metrics\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"F1 Score: {metric['F1'].get():.4f}\")\n",
    "    print(f\"ROCAUC Score: {metric['ROCAUC'].get():.4f}\\n\")\n",
    "\n",
    "# Display performance metrics summary\n",
    "for name, metrics_perf in performance_metrics.items():\n",
    "    avg_latency = sum(metrics_perf['Latency']) / len(metrics_perf['Latency']) if metrics_perf['Latency'] else 0\n",
    "    avg_cpu = sum(metrics_perf['CPU_Usage']) / len(metrics_perf['CPU_Usage']) if metrics_perf['CPU_Usage'] else 0\n",
    "    avg_memory = sum(metrics_perf['Memory_Usage']) / len(metrics_perf['Memory_Usage']) if metrics_perf['Memory_Usage'] else 0\n",
    "    avg_total_time = sum(metrics_perf['Total_Time']) / len(metrics_perf['Total_Time']) if metrics_perf['Total_Time'] else 0\n",
    "\n",
    "    print(f'--- {name} Performance ---')\n",
    "    print(f\"Average Latency per Sample: {avg_latency * 1000:.4f} ms\")\n",
    "    print(f\"Average CPU Usage: {avg_cpu:.2f}%\")\n",
    "    print(f\"Average Memory Usage: {avg_memory:.2f} KB\")  # Modified to KB\n",
    "    print(f\"Average Total Processing Time per Sample: {avg_total_time * 1000:.4f} ms\\n\")\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Enhanced Plotting Function to handle multiple classifiers side by side\n",
    "def plot_performance_side_by_side(performance_metrics, classifier_names, window_size=50000):\n",
    "    metrics_names = ['Memory_Usage']\n",
    "    num_classifiers = len(classifier_names)\n",
    "    \n",
    "    # Create subplots arranged side by side with shared y-axis\n",
    "    fig, axes = plt.subplots(1, num_classifiers, figsize=(15, 6), sharey=True)\n",
    "    \n",
    "    # If there's only one classifier, axes might not be a list\n",
    "    if num_classifiers == 1:\n",
    "        axes = [axes]\n",
    "    \n",
    "    for ax, classifier_name in zip(axes, classifier_names):\n",
    "        for metric in metrics_names:\n",
    "            metric_series = pd.Series(performance_metrics[classifier_name][metric])\n",
    "            rolling_metric = metric_series.rolling(window=window_size).mean()\n",
    "            ax.plot(rolling_metric, label=f'{metric} (Rolling Mean, window={window_size})')\n",
    "        \n",
    "        # Set titles and labels with bold font\n",
    "        ax.set_title(f'{classifier_name} Performance', fontweight='bold', fontsize=14)\n",
    "        ax.set_xlabel('Number of Instances', fontweight='bold', fontsize=12)\n",
    "        ax.set_ylabel('Memory Usage', fontweight='bold', fontsize=12)\n",
    "        \n",
    "        # Make ticks bold\n",
    "        ax.tick_params(axis='both', which='major', labelsize=10, width=2)\n",
    "        for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "            label.set_fontweight('bold')\n",
    "        \n",
    "        # Make legend bold using prop\n",
    "        ax.legend(prop={'weight': 'bold'})\n",
    "        ax.grid(True)\n",
    "\n",
    "    # Force y-axis tick labels to be bold on all subplots\n",
    "    for ax in axes:\n",
    "        for tick in ax.yaxis.get_ticklabels():\n",
    "            tick.set_fontweight('bold')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('/Users/promisea/SAMKNN/ALMASWA/BoT_Memory.png')\n",
    "    plt.show()\n",
    "\n",
    "# Example: Plot performance metrics for both classifiers side by side\n",
    "plot_performance_side_by_side(performance_metrics, ['ALMAClassifier', 'ALMASWAClassifier'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004e567a-e0ea-4bd2-b7b4-03913e8b33ff",
   "metadata": {},
   "source": [
    "### Processing Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "858d7307-a2f8-4b25-8b36-32b75044e095",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples per class: Counter({1: 586241, 0: 13859})\n",
      "Processed 100000 samples. Throughput: 11433.45 samples/sec.\n",
      "Processed 200000 samples. Throughput: 11921.56 samples/sec.\n",
      "Processed 300000 samples. Throughput: 12071.40 samples/sec.\n",
      "Processed 400000 samples. Throughput: 11988.53 samples/sec.\n",
      "Processed 500000 samples. Throughput: 11906.10 samples/sec.\n",
      "Processed 600000 samples. Throughput: 11893.44 samples/sec.\n",
      "\n",
      "Total samples processed: 600100\n",
      "Total elapsed time: 50.46 seconds\n",
      "Throughput: 11892.82 samples/second\n",
      "\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9240\n",
      "F1 Score: 0.9600\n",
      "ROCAUC Score: 0.6970\n",
      "\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9821\n",
      "F1 Score: 0.9909\n",
      "ROCAUC Score: 0.7273\n",
      "\n",
      "--- ALMAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0242 ms\n",
      "Average CPU Usage: 1.75%\n",
      "Average Memory Usage: -0.25 KB\n",
      "Average Total Processing Time per Sample: 0.0400 ms\n",
      "\n",
      "--- ALMASWAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0225 ms\n",
      "Average CPU Usage: 1.75%\n",
      "Average Memory Usage: -0.25 KB\n",
      "Average Total Processing Time per Sample: 0.0400 ms\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from sklearn.utils import shuffle\n",
    "from river import compose, preprocessing, linear_model, metrics, stream\n",
    "import time\n",
    "import psutil\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ---------------------------\n",
    "# Step 1: Initialize Classifiers and Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()  # Ensure ALMASWAClassifier is correctly imported or defined\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'F1': metrics.F1(),\n",
    "        'ROCAUC': metrics.ROCAUC()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize performance tracking dictionaries\n",
    "performance_metrics = {\n",
    "    name: {\n",
    "        'Latency': [],             # Time taken per sample (predict + learn)\n",
    "        'Total_Time': [],          # Cumulative processing time per sample\n",
    "        'CPU_Usage': [],           # CPU usage in percentage\n",
    "        'Memory_Usage': []         # Memory usage in KB (Modified from MB to KB)\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize overall tracking variables\n",
    "total_samples = 0\n",
    "start_time = time.time()\n",
    "\n",
    "# Initialize psutil process for monitoring\n",
    "process = psutil.Process()\n",
    "\n",
    "# ---------------------------\n",
    "# Step 2: Load and Prepare Data\n",
    "# ---------------------------\n",
    "\n",
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT.csv\")\n",
    "print('Number of samples per class:', Counter(data['Label']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# ---------------------------\n",
    "# Step 3: Training Loop with Performance Measurement\n",
    "# ---------------------------\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    total_samples += 1\n",
    "    sample_start_time = time.time()\n",
    "\n",
    "    # Get current CPU and memory usage before processing\n",
    "    cpu_before = process.cpu_percent(interval=None)\n",
    "    memory_before = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    for name, model in classifiers.items():\n",
    "        clf_start_time = time.time()\n",
    "\n",
    "        # Predict\n",
    "        y_pred = model.predict_one(x)\n",
    "\n",
    "        # Learn\n",
    "        model.learn_one(x, true_y)\n",
    "\n",
    "        # Calculate latency for this classifier\n",
    "        clf_latency = time.time() - clf_start_time\n",
    "\n",
    "        # Update metrics\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['F1'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['ROCAUC'].update(true_y, y_pred)\n",
    "\n",
    "        # Record latency\n",
    "        performance_metrics[name]['Latency'].append(clf_latency)\n",
    "\n",
    "    # Get current CPU and memory usage after processing\n",
    "    cpu_after = process.cpu_percent(interval=None)\n",
    "    memory_after = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    # Calculate CPU and memory usage during processing\n",
    "    cpu_usage = cpu_after - cpu_before\n",
    "    memory_usage = memory_after - memory_before\n",
    "\n",
    "    # Record CPU and memory usage for each classifier (approximate)\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['CPU_Usage'].append(cpu_usage / len(classifiers))\n",
    "        performance_metrics[name]['Memory_Usage'].append(memory_usage / len(classifiers))\n",
    "\n",
    "    # Calculate total processing time for this sample\n",
    "    total_time = time.time() - sample_start_time\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['Total_Time'].append(total_time / len(classifiers))\n",
    "\n",
    "    # Optional: Print progress every 100,000 samples\n",
    "    if (i + 1) % 100000 == 0:\n",
    "        elapsed_time = time.time() - start_time\n",
    "        throughput = total_samples / elapsed_time\n",
    "        print(f'Processed {i + 1} samples. Throughput: {throughput:.2f} samples/sec.')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 4: Calculate Throughput\n",
    "# ---------------------------\n",
    "\n",
    "# Calculate total elapsed time\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time - start_time\n",
    "\n",
    "# Calculate throughput (samples per second)\n",
    "throughput = total_samples / elapsed_time\n",
    "print(f'\\nTotal samples processed: {total_samples}')\n",
    "print(f'Total elapsed time: {elapsed_time:.2f} seconds')\n",
    "print(f'Throughput: {throughput:.2f} samples/second\\n')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 5: Display Results\n",
    "# ---------------------------\n",
    "\n",
    "# Display classification metrics\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"F1 Score: {metric['F1'].get():.4f}\")\n",
    "    print(f\"ROCAUC Score: {metric['ROCAUC'].get():.4f}\\n\")\n",
    "\n",
    "# Display performance metrics summary\n",
    "for name, metrics_perf in performance_metrics.items():\n",
    "    avg_latency = sum(metrics_perf['Latency']) / len(metrics_perf['Latency']) if metrics_perf['Latency'] else 0\n",
    "    avg_cpu = sum(metrics_perf['CPU_Usage']) / len(metrics_perf['CPU_Usage']) if metrics_perf['CPU_Usage'] else 0\n",
    "    avg_memory = sum(metrics_perf['Memory_Usage']) / len(metrics_perf['Memory_Usage']) if metrics_perf['Memory_Usage'] else 0\n",
    "    avg_total_time = sum(metrics_perf['Total_Time']) / len(metrics_perf['Total_Time']) if metrics_perf['Total_Time'] else 0\n",
    "\n",
    "    print(f'--- {name} Performance ---')\n",
    "    print(f\"Average Latency per Sample: {avg_latency * 1000:.4f} ms\")\n",
    "    print(f\"Average CPU Usage: {avg_cpu:.2f}%\")\n",
    "    print(f\"Average Memory Usage: {avg_memory:.2f} KB\")  # Modified to KB\n",
    "    print(f\"Average Total Processing Time per Sample: {avg_total_time * 1000:.4f} ms\\n\")\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Enhanced Plotting Function to handle multiple classifiers side by side\n",
    "def plot_performance_side_by_side(performance_metrics, classifier_names, window_size=50000):\n",
    "    metrics_names = ['Total_Time']\n",
    "    num_classifiers = len(classifier_names)\n",
    "    \n",
    "    # Create subplots arranged side by side with shared y-axis\n",
    "    fig, axes = plt.subplots(1, num_classifiers, figsize=(15, 6), sharey=True)\n",
    "    \n",
    "    # If there's only one classifier, axes might not be a list\n",
    "    if num_classifiers == 1:\n",
    "        axes = [axes]\n",
    "    \n",
    "    for ax, classifier_name in zip(axes, classifier_names):\n",
    "        for metric in metrics_names:\n",
    "            metric_series = pd.Series(performance_metrics[classifier_name][metric])\n",
    "            rolling_metric = metric_series.rolling(window=window_size).mean()\n",
    "            ax.plot(rolling_metric, label=f'{metric} (Rolling Mean, window={window_size})')\n",
    "        \n",
    "        # Set titles and labels with bold font\n",
    "        ax.set_title(f'{classifier_name} Performance', fontweight='bold', fontsize=14)\n",
    "        ax.set_xlabel('Number of Instances', fontweight='bold', fontsize=12)\n",
    "        ax.set_ylabel('Total Processing Time (s)', fontweight='bold', fontsize=12)\n",
    "        \n",
    "        # Make ticks bold\n",
    "        ax.tick_params(axis='both', which='major', labelsize=10, width=2)\n",
    "        for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "            label.set_fontweight('bold')\n",
    "        \n",
    "        # Make legend bold using prop\n",
    "        ax.legend(prop={'weight': 'bold'})\n",
    "        ax.grid(True)\n",
    "\n",
    "    # Force y-axis tick labels to be bold on all subplots\n",
    "    for ax in axes:\n",
    "        for tick in ax.yaxis.get_ticklabels():\n",
    "            tick.set_fontweight('bold')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('/Users/promisea/SAMKNN/ALMASWA/BoT_Time.png')\n",
    "    plt.show()\n",
    "\n",
    "# Example: Plot performance metrics for both classifiers side by side\n",
    "plot_performance_side_by_side(performance_metrics, ['ALMAClassifier', 'ALMASWAClassifier'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "089bef1e-4192-4d62-961c-36f24b156fa9",
   "metadata": {},
   "source": [
    "## Experiment 7: Effect of Varying SWA Frequency and SWA Start on performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "389fa61a-abdd-445f-8017-c183b8107179",
   "metadata": {},
   "source": [
    "### Varying SWA Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "77f2e49a-2ab0-407e-a072-9feb133bb2ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluating SWA Frequency: 10\n",
      "Final Recall for SWA Frequency 10: 0.9945\n",
      "\n",
      "Evaluating SWA Frequency: 50\n",
      "Final Recall for SWA Frequency 50: 0.9944\n",
      "\n",
      "Evaluating SWA Frequency: 100\n",
      "Final Recall for SWA Frequency 100: 0.9943\n",
      "\n",
      "Evaluating SWA Frequency: 200\n",
      "Final Recall for SWA Frequency 200: 0.9931\n",
      "\n",
      "Evaluating SWA Frequency: 500\n",
      "Final Recall for SWA Frequency 500: 0.9905\n",
      "\n",
      "Evaluating SWA Frequency: 1000\n",
      "Final Recall for SWA Frequency 1000: 0.9875\n",
      "\n",
      "Evaluating SWA Frequency: 5000\n",
      "Final Recall for SWA Frequency 5000: 0.9693\n",
      "\n",
      "Evaluating SWA Frequency: 10000\n",
      "Final Recall for SWA Frequency 10000: 0.9511\n",
      "\n",
      "=== Experiment Results ===\n",
      "SWA Frequency: 10 | Recall: 0.9945\n",
      "SWA Frequency: 50 | Recall: 0.9944\n",
      "SWA Frequency: 100 | Recall: 0.9943\n",
      "SWA Frequency: 200 | Recall: 0.9931\n",
      "SWA Frequency: 500 | Recall: 0.9905\n",
      "SWA Frequency: 1000 | Recall: 0.9875\n",
      "SWA Frequency: 5000 | Recall: 0.9693\n",
      "SWA Frequency: 10000 | Recall: 0.9511\n",
      "\n",
      "Optimal SWA Frequency: 10 with Recall: 0.9945\n"
     ]
    },
    {
     "data": {
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unWrUqGHXECUtfpYUK1ZMy5Yts7sNGDDAbkzv3r3t7s+dO1c+Pj5q0qSJ3XysUqWKvLy8bPPx999/140bN/TSSy/ZLfnr16/ffdWWnGXLluny5cvq2LGj3Ws7OzurRo0aSf4tSFKvXr3s7terV09Hjhyx3Z83b54sFouGDRuW5LGJdfv4+Kh169aaNWuW7ex9fHy85syZoyeeeEK5cuVK9TEBuIVwBSDF4uPjNXv2bDVs2FBHjx7VoUOHdOjQIdWoUUNnz55VeHj4Az1/hw4dFBkZqSVLlmjGjBlq2bLlXcNHSmvx8fHRqFGjdOzYMR07dkzffvutSpcurc8//1zvvvuubZyvr6/Kli1rF6AqVapkCzK1a9e22+fm5mZ3XdScOXMUFxenSpUq2Wq6dOmSatSoYbc0MPE6r+jo6FS/XydOnFD37t2VN29e2/UYiddBRUZGSroVAj788EMtWbJE+fPnV/369TVq1CjbL8TSresx2rZtqxEjRsjPz0+tW7fW5MmT7ULngzh48KCMMSpVqpT8/f3tbvv27dO5c+fsxhcsWDDZa37uZd68eVq2bJlmzpypmjVr6ty5c3bh8+DBg5Kkbt26Janhm2++UWxsrCIjI3X+/HlFRUXZXUOXnISEBH3yyScqVaqU3N3d5efnJ39/f/3xxx+29/5BtW/fXtevX9eiRYsk3fo6gV9++cV2jZuUPp9dWFiYfv31V12+fFmrV69Wnz59dPz4cbVs2dLus7r9uqvLly9r7969tusaa9eurZs3b2rz5s06evSoTp8+bfdHiLT6WZIrVy41btzY7hYSEmLb7+LiokKFCtk95uDBg4qMjFRAQECSuXDlyhXbMR4/flySknxNhL+/v91y05RInIePPvpoktf+7bffkvxbSLx+6nZ58uSx+0PJ4cOHVaBAAeXNm/eer921a1edOHFCa9askXQrPJ49e1ZdunRJ1bEAsEcrdgAplthefPbs2Zo9e3aS/TNmzFDTpk1T/fxBQUFq0KCBRo8erXXr1mnevHnpUkvRokX17LPPqk2bNipRooRmzJih9957z7a/bt26mjBhgi5fvpykFXrt2rU1adIkxcXFae3atapSpYqsVqvd60rJN8+Qbl2rU6JECZUpU0aStHv3bj3xxBN3f1PuIj4+Xk2aNNGlS5f05ptvqkyZMsqVK5dOnjyp7t272zUG6devn1q1aqWFCxfq119/1ZAhQzRy5EgtX75clSpVsn2H2MaNG7V48WL9+uuvevbZZzV69Ght3LhRXl5eKa7vdgkJCbbv+0ru+rI7n//2UHS/6tevLz8/P0lSq1atVL58eXXu3Fnbtm2Tk5OT7f346KOPVLFixWSfw8vLS5cuXbqv1/vggw80ZMgQPfvss3r33XeVN29eOTk5qV+/fnbv/YOoWbOmihUrpu+//16dOnXS4sWLdf36dbVv3942Jj0/O09PT9WrV0/16tWTn5+fRowYoSVLlti+w+r2ry5IPENYq1YtSZKfn59KlSqltWvX2pqW3B6u0vtnSaLbzzAmSkhIUEBAwF2bZ9wZZu7H3b4UOrnGE9Kt664CAwOTjL/9zJ6kVF+PmZywsDDlz59f06dPV/369TV9+nQFBgaqcePGafYaQE5GuAKQYjNmzFBAQIC++OKLJPvmz5+vBQsWaMKECan65ThRp06d9Pzzz8vX11fNmzdP11ry5Mmj4ODgJBd5161bV+PHj9fvv/+uHTt22JYTSrfC1fXr1/Xzzz/ryJEjtgvkJeno0aNav369+vbtazuDlCghIUFdunTRzJkzNXjwYNWtW1d58uTRrFmz9NZbb6X4l6jdu3frwIEDmjp1ql0Di2XLliU7Pjg4WK+99ppee+01HTx4UBUrVtTo0aM1ffp025iaNWuqZs2aev/99zVz5kx17txZs2fPtvsOq9QIDg6WMUbFixdP8h1H6cHLy0vDhg1Tjx499P3336tDhw4KDg6WdOuM4b1+mfT395e3t3eSOXGnH374QQ0bNtS3335rt/3y5cu2kJcW2rVrp7FjxyoqKkpz5sxRsWLFVLNmzSTj0uuzS5S4RPL06dO2bQEBAbYAlStXLoWEhNiWKUr/O8v7999/y9nZ2Ra8pIz5WXI3wcHB+v3331WnTp17Pn9iI5yDBw+qRIkStu3nz59PssQ28UzW5cuX7d6DxLNft7+2dOu9S6tQExwcrF9//VWXLl2659krZ2dnderUSVOmTNGHH36ohQsXqmfPnmka4ICcjGWBAFLk+vXrmj9/vlq2bKmnnnoqya1v376Kjo62LWFKraeeekrDhg3Tl19+edelYSmtZdeuXbpw4UKS5zl+/LgiIiJUunRpu+2Jf2EfM2aM4uLi7M5cFStWTEFBQRo1apTdWOl/Z60GDBiQpKZ27dopNDTUNsbT01Nvvvmm9u3bpzfffDPZLobTp0+362J3u8RfiG5/nDFGY8eOtRt37dq1JF/AHBwcrNy5c9uWjv3zzz9JXj/x7E5aLA188skn5ezsrBEjRiR5HWOMLl68+MCvcafOnTurUKFC+vDDDyVJVapUUXBwsD7++GO7a5gSnT9/XpLk5OSkJ554QosXL7a1sb6zXunW+3/nscydO1cnT55M0+No3769YmNjNXXqVC1dujTJ1xLc72d3+PDhJG3pk3O35Xi//PKLJCX7b2Xnzp367bffknzZde3atbVhwwatWbNGjzzyiG2Jb0b9LLmbdu3aKT4+3m45cKKbN2/aulc2btxYrq6uGjdunN17/OmnnyZ5XGJoWr16tW3b1atXNXXqVLtxYWFh8vb21gcffJDsNXKJ8zAl2rZtK2OMRowYkWTfnXOjS5cu+ueff/Tiiy/qypUrdAkE0hBnrgCkyKJFixQdHa3HH3882f01a9aUv7+/ZsyYYbdsaevWrXZL7hI1aNAgSVtm6da1UYnfpZRWtSxbtkzDhg3T448/rpo1a8rLy0tHjhzRpEmTFBsbm+T1ihQposKFC2vDhg0qVqyYChQoYLe/du3atovIb1/+N2PGDFWsWFGFCxdOtq7HH39cL730krZv367KlSvrjTfe0N69ezV69GitWLFCTz31lAIDA3XmzBktXLhQmzdvTtJEIFGZMmUUHBys119/XSdPnpS3t7fmzZuX5C/qBw4cUKNGjdSuXTuFhITIxcVFCxYs0NmzZ9WhQwdJ0tSpU/Xll1+qTZs2Cg4OVnR0tCZOnChvb+97nj28X8HBwXrvvfc0aNAgHTt2TE888YRy586to0ePasGCBXrhhRf0+uuvP/Dr3M7V1VWvvPKK3njjDS1dulTNmjXTN998o8cee0xly5ZVjx49VLBgQZ08eVIrVqyQt7e3Fi9eLOnWkr/ffvtNoaGheuGFF/Twww/r9OnTmjt3rtauXStfX1+1bNlS77zzjnr06KHatWtr9+7dmjFjht0ZjrRQuXJllSxZUm+//bZiY2Pt/m1J9//ZNWrUSJKStPq+U+vWrVW8eHG1atVKwcHBunr1qn7//XctXrxY1apVU6tWrezG161bV5MnT9aWLVvUp08fu321a9dWZGSkIiMj9dJLL9m2p/ZnSVoJDQ3Viy++qJEjR2rnzp1q2rSpXF1ddfDgQc2dO1djx47VU089ZftOqZEjR6ply5Zq3ry5duzYoSVLliQ5O9m0aVMVKVJEzz33nN544w05Oztr0qRJ8vf3t33lgXTrzOn48ePVpUsXVa5cWR06dLCN+fnnn1WnTh19/vnnKTqehg0bqkuXLvrss8908OBBNWvWTAkJCVqzZo0aNmyovn372sZWqlRJ5cqV09y5c/Xwww+rcuXKD/ZmAvifjG9QCCAra9WqlbFarbbW1snp3r27cXV1NRcuXDDGmGTbJCfe3n33XWOMfSv2u7mz5XdKazly5IgZOnSoqVmzpgkICDAuLi7G39/ftGjRwixfvjzZx3fs2NFIMp06dUqyb8yYMUaSefjhh23btm3bZiSZIUOG3LWmY8eOGUnm1Vdftdv+ww8/mKZNm5q8efMaFxcXExQUZNq3b29WrlyZ5D24vRV7RESEady4sfHy8jJ+fn6mZ8+eZteuXXatvy9cuGD69OljypQpY3LlymV8fHxMjRo1zPfff297nu3bt5uOHTuaIkWKGHd3dxMQEGBatmxptm7dalenUtmKPdG8efNM3bp1Ta5cuUyuXLlMmTJlTJ8+fcz+/fttY+5nPiT3WufPn0+yLzIy0vj4+JjQ0FDbth07dpgnn3zS5MuXz7i7u5uiRYuadu3amfDwcLvHHj9+3HTt2tX4+/sbd3d3U6JECdOnTx8TGxtrjLnViv21114zQUFBxsPDw9SpU8ds2LDBhIaG2r1ealux3+7tt982kkzJkiWT7Lvfz65o0aKmaNGi//pas2bNMh06dDDBwcHGw8PDWK1WExISYt5++20TFRWVZPz+/ftt/6YPHDhgty8hIcH4+voaSWbOnDm27an5WZKcf5sr3bp1M7ly5brr/q+//tpUqVLFeHh4mNy5c5vy5cubAQMGmFOnTtnGxMfHmxEjRtg+5wYNGpg9e/aYokWL2rViN+bWz4AaNWoYNzc3U6RIETNmzJgkrdgTrVixwoSFhRkfHx9jtVpNcHCw6d69u93ndrf6k5s/N2/eNB999JEpU6aMcXNzM/7+/uaxxx4z27ZtS/L4UaNGGUnmgw8+uOt7AyDlLMb8yzdpAgAAIIlixYqpQYMGmjJliqNLSbGxY8fq1Vdf1bFjx1SkSBFHlwNkG1xzBQAAkIMYY/Ttt98qNDSUYAWkMa65AgAAyAGuXr2qRYsWacWKFdq9e7d+/PFHR5cEZDuEKwAAgBzg/Pnz6tSpk3x9ffXWW2/dtZkIgNTjmisAAAAASANccwUAAAAAaYBwBQAAAABpgGuukpGQkKBTp04pd+7cslgsji4HAAAAgIMYYxQdHa0CBQrIyene56YIV8k4deqUChcu7OgyAAAAAGQSf/31lwoVKnTPMYSrZOTOnVvSrTfQ29vbobXExcXpt99+U9OmTeXq6urQWpA1MGeQUswZpBRzBinFnEFKZaY5ExUVpcKFC9sywr0QrpKRuBTQ29s7U4QrT09PeXt7O3xiIWtgziClmDNIKeYMUoo5g5TKjHPmfi4XoqEFAAAAAKQBwhUAAAAApAHCFQAAAACkAcIVAAAAAKQBwhUAAAAApAHCFQAAAACkAcIVAAAAAKQBwhUAAAAApAHCFQAAAACkAcIVAAAAAKQBwhUAAAAApAHCFQAAAACkAcIVAAAAAKQBF0cXgLuLTzDadPSStl2wKM/hi3J2cdGFK7EKyG1V9eJ55exkcXSJAAAAAP4f4SqTWrrntEYsjtDpyBhJzvru4Da7/UE+Vg1rFaJm5YIcUyAAAAAAOywLzISW7jmt3tO3/3+wSt7pyBj1mr5d7y7eqw2HLyo+wWRghQAAAADuxJmrTCY+wWjE4gjdb1T6dt0xfbvuGGeyAAAAAAfjzFUms/nopXuesbqbM5Ex6j19u5buOZ0OVQEAAAD4N4SrTOZcdMqDlSTbma4RiyNYIggAAAA4AOEqkwnIbU31Y41uXYu1+eiltCsIAAAAwH0hXGUy1YvnVZCPVQ/SZD21Z78AAAAApB7hKpNxdrJoWKsQSUp1wHqQs18AAAAAUodwlQk1Kxek8c9UVqBPykKSRbe+/6p68bzpUxgAAACAu6IVeybVrFyQmoQEasOhc/ptzSY1rlNdzi4uCt93VpPWHZNFStKu3Uga1ipEzk4PsqgQAAAAQGoQrjIxZyeLahTPq4v7jGoF55Orq6vqlPRT9eJ5NWJxRJKW7b4et/YDAAAAyHiEqywo8azW5qOXdC46Rr4erhq8cI/++ue6Rv92QMMfL+voEgEAAIAch3CVRTk7WVQrOJ/t/gdPlleXbzdr6oZjalWhgG7cTNC56BgF5L51DRZLBQEAAID0RbjKJuqV8teTlQpq/o6Tav/VBt287YuEg3ysGtYqRM3KBTmwQgAAACB7o1tgNpJ4Juv2YCVJZyJj1Hv6di3dc9oRZQEAAAA5AuEqm4hPMBqz7ECy+xKj1ojFEYpPuLPHIAAAAIC0QLjKJjYfvZSke+DtjKTTkTHafPRSxhUFAAAA5CCEq2ziXPTdg1VqxgEAAABIGcJVNhGQ25qm4wAAAACkDOEqm6hePK+CfKy6V8P1IJ9bbdkBAAAApD3CVTbh7GTRsFYhknTXgNUrNJjvuwIAAADSCeEqG2lWLkjjn6msQB/7pX+uzrcC1dQNxxR5Pc4RpQEAAADZHl8inM00KxekJiGB2nz0ks5Fxyggt1XF/DzV9sv1OnL+qvrO3K7J3avJxZlcDQAAAKQlfsPOhpydLKoVnE+tKxZUreB8CvLx0Nddq8rD1VlrDl7Q+7/sc3SJAAAAQLZDuMohyhX00Zh2FSRJk9cd06zNJxxcEQAAAJC9EK5ykMfKB6l/k4ckSUMW7tHGIxcdXBEAAACQfRCucpiXHi2plo8E6WaCUe/p23Ti4jVHlwQAAABkC4SrHMZisejjpyvokUI++udanJ7/bouiY+ggCAAAADwowlUOZHV11tddqiogt7sOnL2ifrN3Kj7BOLosAAAAIEsjXOVQgT5Wfd21qtxdnBT+5zmN+vVPR5cEAAAAZGmEqxysYmFfjXrqEUnSV6uOaN62vx1cEQAAAJB1Ea5yuNYVC+qlR0tKkgbN361txy85uCIAAAAgayJcQa82fkhhZfPrRnyCXpy2TScvX3d0SQAAAECWQ7iCnJwsGtOuoh4O8taFKzf0/NStuhp709FlAQAAAFkK4QqSpFzuLvqmW1X5eblp3+kovfb9LiXQQRAAAAC4b4Qr2BT09dBXXarIzdlJS/ee0ae/H3B0SQAAAECWQbiCnSpF8+qDJ8tLkj5bfkiLdp1ycEUAAABA1kC4QhJPVSmkF+uXkCS9MXeXdv11WfEJRhsOX9SPO09qw+GLfOkwAAAAcAcXRxeAzGlAszI6dO6Kwv88py7fbpLV1VnnomNt+4N8rBrWKkTNygU5sEoAAAAg8+DMFZLl7GTRpx0qKsjHqqiYm3bBSpLORMao9/TtWrrntIMqBAAAADIXwhXuytPNRTfjk1/+l7h1xOIIlggCAAAAIlzhHjYfvaTzV2Lvut9IOh0Zo81HL2VcUQAAAEAmRbjCXZ2LjknTcQAAAEB25vBw9cUXX6hYsWKyWq2qUaOGNm/efNexcXFxeueddxQcHCyr1aoKFSpo6dKldmOio6PVr18/FS1aVB4eHqpdu7a2bNmS3oeRLQXktqbpOAAAACA7c2i4mjNnjvr3769hw4Zp+/btqlChgsLCwnTu3Llkxw8ePFhfffWVxo0bp4iICPXq1Utt2rTRjh07bGOef/55LVu2TNOmTdPu3bvVtGlTNW7cWCdPnsyow8o2qhfPqyAfqyz3GOPv5a7qxfNmWE0AAABAZuXQcDVmzBj17NlTPXr0UEhIiCZMmCBPT09NmjQp2fHTpk3TW2+9pebNm6tEiRLq3bu3mjdvrtGjR0uSrl+/rnnz5mnUqFGqX7++SpYsqeHDh6tkyZIaP358Rh5atuDsZNGwViGSdNeAFRUTp/B9ZzOuKAAAACCTctj3XN24cUPbtm3ToEGDbNucnJzUuHFjbdiwIdnHxMbGymq1X4Lm4eGhtWvXSpJu3ryp+Pj4e4652/PGxv6vcUNUVJSkW8sQ4+LiUnZgaSzx9R1VR6PSfhrXoYLe++VPnYn633uUP7e7fDxddeDsFb0wbZtebVRSvUOLy2K513kuZARHzxlkPcwZpBRzBinFnEFKZaY5k5IaLMYYh/TRPnXqlAoWLKj169erVq1atu0DBgzQqlWrtGnTpiSP6dSpk3bt2qWFCxcqODhY4eHhat26teLj423hqHbt2nJzc9PMmTOVP39+zZo1S926dVPJkiW1f//+ZGsZPny4RowYkWT7zJkz5enpmUZHnLUlGOlwlEVRcZK3qxTsbWSMtPC4k1afuXUCtFK+BHUKTpCbs4OLBQAAANLItWvX1KlTJ0VGRsrb2/ueYx125io1xo4dq549e6pMmTKyWCwKDg5Wjx497JYRTps2Tc8++6wKFiwoZ2dnVa5cWR07dtS2bdvu+ryDBg1S//79bfejoqJUuHBhNW3a9F/fwPQWFxenZcuWqUmTJnJ1dXVoLclpJWnO1r81fPE+7bjopDh3X33Z6daXD8MxMvucQebDnEFKMWeQUswZpFRmmjOJq9ruh8PClZ+fn5ydnXX2rP31OmfPnlVgYGCyj/H399fChQsVExOjixcvqkCBAho4cKBKlChhGxMcHKxVq1bp6tWrioqKUlBQkNq3b2835k7u7u5yd3dPst3V1dXhH2aizFTLnZ6pVVyl8nur94zt2nMqSk9O2KSvulRRlaJ5HF1ajpaZ5wwyJ+YMUoo5g5RiziClMsOcScnrO6yhhZubm6pUqaLw8HDbtoSEBIWHh9stE0yO1WpVwYIFdfPmTc2bN0+tW7dOMiZXrlwKCgrSP//8o19//TXZMUg7NUrk04996qhMYG5duBKrjl9v1A/b/nZ0WQAAAECGcWi3wP79+2vixImaOnWq9u3bp969e+vq1avq0aOHJKlr1652DS82bdqk+fPn68iRI1qzZo2aNWumhIQEDRgwwDbm119/1dKlS3X06FEtW7ZMDRs2VJkyZWzPifRTOK+n5vWurbCy+XUjPkGvz92l93+OUHyCQy7rAwAAADKUQ6+5at++vc6fP6+hQ4fqzJkzqlixopYuXar8+fNLkk6cOCEnp//lv5iYGA0ePFhHjhyRl5eXmjdvrmnTpsnX19c2JjIyUoMGDdLff/+tvHnzqm3btnr//fcdfjoxp8jl7qLxnavo098P6LPlhzRxzVEdOHtF4zpVkreVzwAAAADZl8MbWvTt21d9+/ZNdt/KlSvt7oeGhioiIuKez9euXTu1a9curcpDKjg5WdS/aWk9FJhbr8/dpVUHzuuJL9bpm65VVcLfy9HlAQAAAOnCocsCkb21fKSAfuhVW0E+Vh05f1VPfLFOqw+cd3RZAAAAQLogXCFdlSvoox/71lHlIr6Kirmp7pM3a9Lao3LQ16sBAAAA6YZwhXQXkNuqWS/U1FNVCinBSO/8FKGB83Yr9ma8o0sDAAAA0gzhChnC3cVZHz31iAa3eFhOFmnO1r/UeeImXbgS6+jSAAAAgDRBuEKGsVgser5eCU3qXk25rS7aevwfPT5urfaeinR0aQAAAMADI1whwzUoHaCFfeqohF8unYqM0VPjN2jJ7tOOLgsAAAB4IIQrOESwv5cW/KeO6pXy0/W4ePWesV2fLDugBL5wGAAAAFkU4QoO4+Ppqsndq+m5usUlSWPDD6rPzO26duOmgysDAAAAUo5wBYdycXbSkJYhGvXUI3JzdtKSPWfUdvwG/f3PNUeXBgAAAKQI4QqZQruqhTXrhRry83LTvtNRav35Om05dsnRZQEAAAD3jXCFTKNK0bz6sW9dhQR56+LVG+o0caPmbDnh6LIAAACA+0K4QqZS0NdDP/SupRblgxQXb/TmvN0avmivbsYnOLo0AAAA4J4IV8h0PN1c9HmnSurf5CFJ0pT1x9RjyhZFXouTJMUnGG04fFE/7jypDYcvKp4OgwAAAMgEXBxdAJAci8WilxuV0kP5vfTqnF1ac/CCWn+xVt1qF9PXq4/odGSMbWyQj1XDWoWoWbkgB1YMAACAnI4zV8jUmpUL0rzetVXQ10PHLl7TiMURdsFKks5Exqj39O1auocvIgYAAIDjEK6Q6YUU8Nb8/9SWm7Ml2f2JiwJHLI5giSAAAAAchnCFLOHI+au6EX/34GQknY6M0eajtG8HAACAYxCukCWci47590EpGAcAAACkNcIVsoSA3NY0HQcAAACkNcIVsoTqxfMqyMeq5K+6+p8//r7Md2IBAADAIQhXyBKcnSwa1ipEku4ZsEYu+VOPf75OO/+6nCF1AQAAAIkIV8gympUL0vhnKivQx37pX5CPVV92qqxRbR+Rr6erIk5Hqc2X6zTsxz2KiolzULUAAADIafgSYWQpzcoFqUlIoDYfvaRz0TEKyG1V9eJ55ex063xWo4cD9P4v+zR/+0lN3XBcS/ac0fDHy+qxcoGyWP5tUSEAAACQeoQrZDnOThbVCs6X7L58Xu4a066inqpcSG8v3KOjF67qPzO269EyARrxeFkVzuuZwdUCAAAgp2BZILKl2iX9tOSVenq5USm5Olu0/M9zavrJan216rDiaHgBAACAdEC4QrZldXVW/yYPackr9VW9eF5dj4vXyCV/qtW4tdp+4h9HlwcAAIBshnCFbK9kgJfmvFBTHz31iPJ4uurPM9FqO369hiyk4QUAAADSDuEKOYLFYtHTVQsr/LUGeqpKIRkjTdt4XI1Gr9JPf5ySMcbRJQIAACCLI1whR8mby00fP11BM3vWUAm/XDofHau+M3eox5Qt+uvSNUeXBwAAgCyMcIUcqXawn5b0q6d+jUvJzdlJK/efV5NPVmn8ShpeAAAAIHUIV8ix3F2c1a/xQ1rSr55qlsirmLgEfbj0VsOLbcdpeAEAAICUIVwhxwv299KsnjU1+ukKtoYXT01Yr7cX7FbkdRpeAAAA4P4QrgDdanjRtkohhb/WQE//f8OLGZtOqNHoVVq0i4YXAAAA+HeEK+A2eXO56aOnK2j2CzUV7J9LF67E6uVZO9Rt8haduEjDCwAAANwd4QpIRs0S+fTLK/XUv8lDcnNx0uoDtxpefLnyEA0vAAAAkCzCFXAX7i7OerlRKS19pZ5qB+dT7M0EjVq6Xy0+W6Otxy45ujwAAABkMoQr4F+U8PfSjOdraEy7Csqby00Hzl7RUxM2aND83Yq8RsMLAAAA3EK4Au6DxWLRk5ULKbx/qNpXLSxJmrX5hBqNWakfd56k4QUAAAAIV0BK5Mnlpg+fekTfv1hLJQO8dOHKDb0ye6e6Ttqs4xevOro8AAAAOBDhCkiF6sXz6peX6+n1prcaXqw5eEFNP1mtL1Yc0o2bNLwAAADIiQhXQCq5uTip76Ol9Fu/+qpb0k+xNxP00a+3Gl5soeEFAABAjkO4Ah5QMb9cmvZcdY3tUFF+Xm46eO6Knp6wQQPn/aHL1244ujwAAABkEMIVkAYsFotaVyyo3/uHqmP1Ww0vZm/5S41Gr9KCHX/T8AIAACAHIFwBacjX000jn3xEc3vVUqkAL128ekOvztmlZ77dpKMXaHgBAACQnRGugHRQrVhe/fxyPb0RVlruLk5ad+iiwj5drXHhBxV7M97R5QEAACAdEK6AdOLm4qQ+DUvqt1frq14pP924maDRyw6oxWdrtenIRUeXBwAAgDRGuALSWdF8ufTds9X1WcdK8vNy16FzV9T+640a8MMu/XOVhhcAAADZBeEKyAAWi0WPVyig8P6h6lSjiCTp+61/q9GYVZq3jYYXAAAA2QHhCshAPp6u+qBNec3rXUul8+fWpas39NrcXer8zSYdOX/F0eUBAADgARCuAAeoUjSvfnq5rt5sVkZWVyetP3xRzT5do7G/0/ACAAAgqyJcAQ7i6uyk3g2C9Vu/UIU+5K8b8Qn65PcDemzsGm2k4QUAAECWQ7gCHKxIPk9N6VFNn3eqJP/c7jpy/qo6fL1Rr8/dpUs0vAAAAMgyCFdAJmCxWNTykQL6vX+onqlZRBaL9MO2v9Vo9Er9QMMLAACALIFwBWQiPh6ueu+J8vqhV22VCcytf67F6fW5u9Rx4kYdpuEFAABApka4AjKhKkXzaPFLdTXwsVsNLzYeuaTHPl2jT5YdUEwcDS8AAAAyI8IVkEm5OjupV2iwlr0aqoalbzW8GBt+UM3HrtH6wxccXR4AAADuQLgCMrnCeT01qXs1fdm5sgJyu+vIhavqNHGT+n+/UxevxDq6PAAAAPw/whWQBVgsFjUvH6TfXwtV11pFZbFI87efVKMxq/T91r9oeAEAAJAJEK6ALMTb6qp3WpfT/N63Gl5cvhanAT/8ofZfb9Shc9GOLg8AACBHI1wBWVClIrcaXrzVvIw8XJ21+eglPTZ2jcb8tl+xNLwAAABwCMIVkEW5OjvphfrBWta/vh4tE6C4eKPPlh9Syy82aH+kxdHlAQAA5DiEKyCLK5THU992q6rxnSsrv7e7jl28pi8jnPX6D7t1gYYXAAAAGYZwBWQDFotFj5UP0u/9Q9WlZhFZZPTjrtNqNHqV5mw5oYQEGl4AAACkN8IVkI3ktrpqaIsyerVcvB4OzK3I63F6c95udfh6ow6epeEFAABAeiJcAdlQ0dzS/F41NLjFw/J0c9bmY5fU/LM1+vjX/Yqh4QUAAEC6IFwB2ZSLs5Oer1dCy/qHqvHDtxpefL7ikMI+Xa01B887ujwAAIBsh3AFZHMFfT00sWtVTXimigK9rTp+8Zq6fLtZr8zeofPRNLwAAABIK4QrIAewWCxqVi5Qv78Wqh51isnJIv2485QajV6pWZtpeAEAAJAWCFdADuLl7qJhrcpqYZ86KlfQW1ExNzVo/m61+2qDDtDwAgAA4IEQroAc6JFCvlr4nzoa0jJEudyctfX4P2o+do1GLf2ThhcAAACpRLgCcigXZyc9V7e4lvUPVdOQ/LqZYPTlysNq+slqrTpAwwsAAICUIlwBOVwBXw993bWqvupSRUE+Vp24dE3dJm3WS7N26Fx0jKPLAwAAyDIIVwAkSWFlA7Wsf6ierVNcThZp8a5TajR6lWZsOk7DCwAAgPtAuAJg4+XuoqGtQrSob12VL+ij6JibenvBHj01Yb3+PBPl6PIAAAAyNcIVgCTKFfTRwj51NLzVrYYX209cVsvP1urDpX/q+g0aXgAAACSHcAUgWc5OFnWvU1y/vxaqZmUDdTPBaPzKw2r66Sqt2H/O0eUBAABkOoQrAPcU5OOhCV2qaGLXqirgY9Vfl66rx+Qt6jNzu85F0fACAAAgEeEKwH1pEpJfy/qH6vm6txpe/PzHaTUavUrTNtLwAgAAQCJcAUiBXO4uGtzyVsOLCoV8FB17U0MW7lHbCeu17zQNLwAAQM5GuAKQYuUK+mj+f+poxONl5eXuoh0nLqvluLUa+cs+Xbtx09HlAQAAOAThCkCqODtZ1K12Mf3eP1TNywcqPsHoq9VH1GTMaq34k4YXAAAg5yFcAXgggT5Wfdm5ir7tVlUFfT108vJ19ZiyRX1mbNdZGl4AAIAchHAFIE00eji/lvWvrxfql5Czk0U/7z6txqNX6bsNxxRPwwsAAJADEK4ApBlPNxe91fxhLe5bVxUL+yo69qaG/rhXT45fr72nIh1dHgAAQLoiXAFIcyEFvDWvd22927qscru7aNdfl/X45+v0/s8RuhpLwwsAAJA9Ea4ApAtnJ4u61Cqm318LVYtHghSfYDRxzVE1/WS1wveddXR5AAAAac7h4eqLL75QsWLFZLVaVaNGDW3evPmuY+Pi4vTOO+8oODhYVqtVFSpU0NKlS+3GxMfHa8iQISpevLg8PDwUHBysd999V8ZwzQfgCPm9rfqiU2VN7l7N1vDiualb1Xv6Np2JpOEFAADIPhwarubMmaP+/ftr2LBh2r59uypUqKCwsDCdO5d8G+fBgwfrq6++0rhx4xQREaFevXqpTZs22rFjh23Mhx9+qPHjx+vzzz/Xvn379OGHH2rUqFEaN25cRh0WgGQ0LBOgZf3r68XQWw0vluw5o8ZjVmnKuqM0vAAAANmCQ8PVmDFj1LNnT/Xo0UMhISGaMGGCPD09NWnSpGTHT5s2TW+99ZaaN2+uEiVKqHfv3mrevLlGjx5tG7N+/Xq1bt1aLVq0ULFixfTUU0+padOm9zwjBiBjeLq5aNBjD+unl+qqUhFfXYm9qeGLI9Tmy3Xac5KGFwAAIGtzcdQL37hxQ9u2bdOgQYNs25ycnNS4cWNt2LAh2cfExsbKarXabfPw8NDatWtt92vXrq2vv/5aBw4c0EMPPaRdu3Zp7dq1GjNmzF1riY2NVWxsrO1+VFSUpFvLEOPi4lJ1fGkl8fUdXQeyjqwwZ0r6eWj2c9U0e+vf+njZQf3xd6Qe/3ytutUqqlceDVYud4f9aMqRssKcQebCnEFKMWeQUplpzqSkBotx0MVIp06dUsGCBbV+/XrVqlXLtn3AgAFatWqVNm3alOQxnTp10q5du7Rw4UIFBwcrPDxcrVu3Vnx8vC0cJSQk6K233tKoUaPk7Oys+Ph4vf/++3Yh7k7Dhw/XiBEjkmyfOXOmPD090+BoAdxN5A1pwTEn7bh460S6r5vRU8UTVD4vSwUBAIDjXbt2TZ06dVJkZKS8vb3vOTZL/Xl47Nix6tmzp8qUKSOLxaLg4GD16NHDbhnh999/rxkzZmjmzJkqW7asdu7cqX79+qlAgQLq1q1bss87aNAg9e/f33Y/KipKhQsXVtOmTf/1DUxvcXFxWrZsmZo0aSJXV1eH1oKsISvOmY6SVh+8oGGL9+nvf67rm/3OavJwgIa0KKMgH+u/Ph4PJivOGTgWcwYpxZxBSmWmOZO4qu1+OCxc+fn5ydnZWWfP2rdkPnv2rAIDA5N9jL+/vxYuXKiYmBhdvHhRBQoU0MCBA1WiRAnbmDfeeEMDBw5Uhw4dJEnly5fX8ePHNXLkyLuGK3d3d7m7uyfZ7urq6vAPM1FmqgVZQ1abM41CglS7ZIA+W35QE1cf0bJ957T+8EW9HlZaXWsVk7OTxdElZntZbc7A8ZgzSCnmDFIqM8yZlLy+wxpauLm5qUqVKgoPD7dtS0hIUHh4uN0yweRYrVYVLFhQN2/e1Lx589S6dWvbvmvXrsnJyf6wnJ2dlZCQkLYHACDNebg5681mZfTzy/VUpWgeXb0RrxGLI/TEF+u0+28aXgAAgMzNod0C+/fvr4kTJ2rq1Knat2+fevfuratXr6pHjx6SpK5du9pdK7Vp0ybNnz9fR44c0Zo1a9SsWTMlJCRowIABtjGtWrXS+++/r59//lnHjh3TggULNGbMGLVp0ybDjw9A6pQOzK25L9bSB23Ky9vqot0nI9X6i7V6Z3GErsTedHR5AAAAyXLoNVft27fX+fPnNXToUJ05c0YVK1bU0qVLlT9/fknSiRMn7M5CxcTEaPDgwTpy5Ii8vLzUvHlzTZs2Tb6+vrYx48aN05AhQ/Sf//xH586dU4ECBfTiiy9q6NChGX14AB6Ak5NFnWoUUeOQAL330z4t2nVKk9Yd1ZI9pzX88bIKK5v88mEAAABHcXhDi759+6pv377J7lu5cqXd/dDQUEVERNzz+XLnzq1PP/1Un376aRpVCMCRAnJb9VnHSnqqSiENXrhHJy5d04vTtqlJSH6NeLysCvh6OLpEAAAASQ5eFggA96v+Q/767dX66tMwWC5OFi2LOKvGY1bp27VHdTOeayoBAIDjEa4AZBlWV2e9EVZGv7xST1WL5tG1G/F696cIPfHlOv3x92VHlwcAAHI4whWALOeh/Ln1/Yu19N8nbzW82HMySk98sU7DF+1VdIzjv8kdAADkTIQrAFmSk5NFHaoXUfhrDfRExQJKMNKU9cfUeMwqLd1zWsYYR5cIAAByGMIVgCzNP7e7Pu1QSdOeq66i+Tx1NipWvaZvV8/vturk5euOLg8AAOQghCsA2UK9Uv76tV99vfRoSbk6W/T7vnNqMmaVvllzhIYXAAAgQxCuAGQbVldnvda0tJa8Uk/Vi+XVtRvxeu/nfXr883Xa+ddlR5cHAACyOcIVgGynZEBuzX6hpka1fUQ+Hq6KOB2lNl+u07Af9yiKhhcAACCdEK4AZEtOTha1q1ZY4a+F6slKBWWMNHXDcTUZs0q/7KbhBQAASHuEKwDZmp+Xu8a0r6gZz9dQsf9vePGfGdv13NSt+uvSNUeXBwAAshHCFYAcoU5JPy3tV18vNyolV2eLlv95Tk0/Wa2vVx9WHA0vAABAGiBcAcgxrK7O6t/kIS15pb6qF8+r63Hx+uCXP9Vq3FptP/GPo8sDAABZHOEKQI5TMsBLc16oqY+eekS+nq7680y02o5fryELaXgBAABSj3AFIEeyWCx6umphhfcPVdvKhWSMNG3jcTUavUo//0HDCwAAkHKEKwA5Wj4vd41uV0Eze9ZQCb9cOh8dqz4zt6vHlC00vAAAAClCuAIASbWD/bSkXz31a1xKbs5OWrn/vJp8skoTVtHwAgAA3B/CFQD8P3cXZ/Vr/JCW9KunmiXyKiYuQf9dcqvhxbbjNLwAAAD3RrgCgDsE+3tpVs+aGv10BeX5/4YXT01Yr7cX7FbkdRpeAACA5BGuACAZFotFbasUUvhrDfR0lVsNL2ZsOqFGo1dp8a5TNLwAAABJEK4A4B7y5nLTR09X0KyeNVXCP5cuXInVS7N2qNvkLTpxkYYXAADgfwhXAHAfagXn05JX6ql/k4fk5uKk1QduNbz4cuUhGl4AAABJhCsAuG/uLs56uVEpLX2lnmoH51PszQSNWrpfLT9bq23HLzm6PAAA4GCEKwBIoRL+XprxfA2NaVdBeXO5af/ZaLUdv0GD5u9W5DUaXgAAkFMRrgAgFSwWi56sXEjh/UPVvmphSdKszSfUaMxK/bjzJA0vAADIgQhXAPAA8uRy04dPPaI5L9RUyQAvXbhyQ6/M3qmukzbr+MWrji4PAABkIMIVAKSBGiXy6ZeX6+n1prcaXqw5eEFNP1mtL1Yc0o2bNLwAACAnIFwBQBpxc3FS30dL6bd+9VW3pJ9ibyboo1/3q8Vna7TlGA0vAADI7ghXAJDGivnl0rTnquvT9hWVL5ebDp67oqcnbNDAeX/o8rUbji4PAACkE8IVAKQDi8WiJyoVVPhroepY/VbDi9lb/lKj0au0cAcNLwAAyI4IVwCQjnw93TTyyUc0t1ctlQrw0sWrN9Rvzk51+Xazjl2g4QUAANkJ4QoAMkC1Ynn188v19EZYabm7OGntoQtq+ulqjQs/qNib8Y4uDwAApAHCFQBkEDcXJ/VpWFK/vVpf9Ur56cbNBI1edkAtPlurTUcuOro8AADwgAhXAJDBiubLpe+era6xHSrKz8tNh85dUfuvN2rAD7v0z1UaXgAAkFURrgDAASwWi1pXLKjw/g3UqUYRSdL3W/9WozGrNH/73zS8AAAgCyJcAYAD+Xi66oM25TWvdy2Vzp9bl67eUP/vd6nzN5t05PwVR5cHAABSgHAFAJlAlaJ59dPLdfVmszKyujpp/eGLajZ2jcb+TsMLAACyCsIVAGQSrs5O6t0gWL/1C1X9h/x142aCPvn9gB4bu0YbaXgBAECmR7gCgEymSD5PTe1RTeM6VpKfl7uOnL+qDl9v1Otzd+kSDS8AAMi0CFcAkAlZLBa1qlBA4a+FqnONIrJYpB+2/a1Go1fqh200vAAAIDMiXAFAJubj4ar325TXD71qq0xgbv1zLU6vz92ljhM36jANLwAAyFQIVwCQBVQpmkeLX6qrgY/danix8cglPfbpGn2y7IBi4mh4AQBAZkC4AoAswtXZSb1Cg7Xs1VA1KO2vG/EJGht+UM3HrtH6wxccXR4AADke4QoAspjCeT01uXs1fdGpsvxzu+vIhavqNHGTXvuehhcAADgS4QoAsiCLxaIWjwQp/LVQdalZVBaLNG/733p09Ep9v/UvGl4AAOAAhCsAyMK8ra5694lymt/7VsOLy9fiNOCHP9Th6406dI6GFwAAZCTCFQBkA5WK3Gp48VbzMvJwddamo5f02NjVGvPbfhpeAACQQVzud+Aff/xx30/6yCOPpKoYAEDquTo76YX6wWpePkhDf9yr5X+e02fLD2nxH6f13hPlVKekn6NLBAAgW7vvcFWxYkVZLJa7ruNP3GexWBQfz19JAcBRCuXx1LfdqmrpnjMavnivjl64qs7fbNKTlQrq7RYPK5+Xu6NLBAAgW7rvcHX06NH0rAMAkIYsFoseKx+kuqX8NPq3A5q64Zjm7zip5fvPadBjZfR0lcJycrI4ukwAALKV+w5XRYsWTc86AADpILfVVcMfL6snKhXUW/N3K+J0lN6ct1vztp3U+23KqVT+3I4uEQCAbOO+w9WiRYvu+0kff/zxVBUDAEgfFQv7alHfOpqy/phG/3ZAm49dUvPP1ujF+sF6sR5/PAMAIC3cd7h64okn7msc11wBQObk4uyk5+uVULNygRq+aK9+33dOn684pEW7TqpFfouaO7pAAACyuPtuxZ6QkHBfN4IVAGRuhfJ4amLXqprwTBUFelt14tJ1jd/nrNfm7taFK7GOLg8AgCyL77kCgBzIYrGoWblA/f5aqLrVKiKLjBb9cVqNRq/S7M0nlJCQfGdYAABwd/e9LPBOV69e1apVq3TixAnduHHDbt/LL7/8wIUBANKfl7uLBjcvI78rR7T0Yh7tPRWtgfN364dtf+uDJ8vrIRpeAABw31IVrnbs2KHmzZvr2rVrunr1qvLmzasLFy7I09NTAQEBhCsAyGKKeEk/tK2hmVtPafRv+7X1+D9qPnaNXqhfQi83KiWrq7OjSwQAINNL1bLAV199Va1atdI///wjDw8Pbdy4UcePH1eVKlX08ccfp3WNAIAM4OLspOfqFtfv/UPVJCS/biYYfbnysJp+slqrDpx3dHkAAGR6qQpXO3fu1GuvvSYnJyc5OzsrNjZWhQsX1qhRo/TWW2+ldY0AgAxUwNdDE7tW1VddqijIx6oTl66p26TNennWDp2LjnF0eQAAZFqpCleurq5ycrr10ICAAJ04cUKS5OPjo7/++ivtqgMAOExY2UAt6x+qZ+sUl5NFWrTrlBqPXqWZm2h4AQBAclIVripVqqQtW7ZIkkJDQzV06FDNmDFD/fr1U7ly5dK0QACA43i5u2hoqxAt6ltX5Qv6KCrmpt5asFtPf7VBf56JcnR5AABkKqkKVx988IGCgoIkSe+//77y5Mmj3r176/z58/rqq6/StEAAgOOVK+ijhX3qaFirEOVyc9a24/+o5Wdr9eHSP3X9Bt9vCACAlMpugVWrVrX9d0BAgJYuXZpmBQEAMidnJ4t61CmuZuUCNXzRXv2696zGrzysn/44pXdbl1OD0gGOLhEAAIdK1Zmro0eP6uDBg0m2Hzx4UMeOHXvQmgAAmViQj4e+6lJVE7tWVQEfq/66dF3dJ29R35nbdS6KhhcAgJwrVeGqe/fuWr9+fZLtmzZtUvfu3R+0JgBAFtAkJL+W9Q/V83VvNbz46Y/TajRmlaZvPE7DCwBAjpSqcLVjxw7VqVMnyfaaNWtq586dD1oTACCLyOXuosEtbzW8qFDIR9ExNzV44R61nbBe+07T8AIAkLOkKlxZLBZFR0cn2R4ZGan4eC5sBoCcplxBH83/Tx2NeLysvNxdtOPEZbUct1Yjl+zTtRs3HV0eAAAZIlXhqn79+ho5cqRdkIqPj9fIkSNVt27dNCsOAJB1ODtZ1K12Mf3eP1SPlQtUfILRV6uOqOknq7Xiz3OOLg8AgHSXqm6BH374oerXr6/SpUurXr16kqQ1a9YoKipKy5cvT9MCAQBZS6CPVeOfqaLwfWc19Me9+vuf6+oxZYtalA/S0FYhyu9tdXSJAACki1SduQoJCdEff/yhdu3a6dy5c4qOjlbXrl31559/8iXCAABJUqOH82tZ//p6oX4JOTtZ9PPu02o8epWmbTimeBpeAACyoVSduZKkAgUK6IMPPkjLWgAA2Yynm4veav6wWlcsoLcW7NGuvy5ryI979cP2k/qgTTmVLeDj6BIBAEgzqTpzJd1aBvjMM8+odu3aOnnypCRp2rRpWrt2bZoVBwDIHsoW8NH83rX1buuyyu3uol1/Xdbjn6/TB7/Q8AIAkH2kKlzNmzdPYWFh8vDw0Pbt2xUbGyvpVrdAzmYBAJLj7GRRl1rF9PtroWpRPkjxCUZfrz6iJmNWK3zfWUeXBwDAA0tVuHrvvfc0YcIETZw4Ua6urrbtderU0fbt29OsOABA9pPf26ovOlfW5O7VVNDXQycvX9dzU7eq9/RtOhMZ4+jyAABItVSFq/3796t+/fpJtvv4+Ojy5csPWhMAIAdoWCZAy/rX14uhtxpeLNlzRo3HrNKUdUdpeAEAyJJSFa4CAwN16NChJNvXrl2rEiVKPHBRAICcwdPNRYMee1g/vVRXFQv76krsTQ1fHKE2X67TnpORji4PAIAUSVW46tmzp1555RVt2rRJFotFp06d0owZM/Taa6+pd+/eaV0jACCbezjIW/N719Z7T5RTbquL/vg7Uo9/vlbv/RShq7E0vAAAZA2pasU+cOBAJSQkqFGjRrp27Zrq168vd3d3vfHGG3r++efTukYAQA7g5GTRMzWLqmlIfr3zU4R++uO0vll7VL/sPq13WpdT45D8ji4RAIB7StWZK4vForfffluXLl3Snj17tHHjRp0/f14+Pj4qXrx4WtcIAMhBAryt+rxTZU3pUU2F83roVGSMnv9uq16ctlWnI687ujwAAO4qReEqNjZWgwYNUtWqVVWnTh398ssvCgkJ0d69e1W6dGmNHTtWr776anrVCgDIQRqUDtBv/ULVu0GwXJws+nXvWTUevUqTaXgBAMikUhSuhg4dqvHjx6tYsWI6evSonn76ab3wwgv65JNPNHr0aB09elRvvvlmetUKAMhhPNyc9WazMvrp5bqqXMRXV2/Ea8TiCD3xxTrt/puGFwCAzCVF4Wru3Ln67rvv9MMPP+i3335TfHy8bt68qV27dqlDhw5ydnZOrzoBADlYmUBv/dCrtt5vc6vhxe6TkWr9xVq9szhCV2h4AQDIJFIUrv7++29VqVJFklSuXDm5u7vr1VdflcViSZfiAABI5ORkUecaRRX+Wqger1BACUaatO6omoxZpd/2nnF0eQAApCxcxcfHy83NzXbfxcVFXl5eaV4UAAB3E5Dbqs86VtJ3z1ZXkbyeOh0ZoxembdML323Vqcs0vAAAOE6KWrEbY9S9e3e5u7tLkmJiYtSrVy/lypXLbtz8+fPTrkIAAJJR/yF//fZqfY1bflBfrTqi3yLOat2hC+rftLS61SoqF+dUNcQFACDVUhSuunXrZnf/mWeeSdNiAABICaurs94IK6PWFQvqrfm7tfX4P3r3pwgt2PG3PmhTXo8U8nV0iQCAHCRF4Wry5MnpVQcAAKn2UP7c+v7FWpqz9S+N/GWf9pyM0hNfrFPXWsX0WtOHlNvq6ugSAQA5QKZYM/HFF1+oWLFislqtqlGjhjZv3nzXsXFxcXrnnXcUHBwsq9WqChUqaOnSpXZjihUrJovFkuTWp0+f9D4UAICDODlZ1LF6EYW/1kBPVLzV8GLK+mNqMma1lu45I2P4biwAQPpyeLiaM2eO+vfvr2HDhmn79u2qUKGCwsLCdO7cuWTHDx48WF999ZXGjRuniIgI9erVS23atNGOHTtsY7Zs2aLTp0/bbsuWLZMkPf300xlyTAAAx/HP7a5PO1TStOeqq2g+T52JilGv6dvU87ttOknDCwBAOnJ4uBozZox69uypHj16KCQkRBMmTJCnp6cmTZqU7Php06bprbfeUvPmzVWiRAn17t1bzZs31+jRo21j/P39FRgYaLv99NNPCg4OVmhoaEYdFgDAweqV8tev/errpUdLytXZot/3nVWTMav0zZojuhmf4OjyAADZUIquuUprN27c0LZt2zRo0CDbNicnJzVu3FgbNmxI9jGxsbGyWq122zw8PLR27dq7vsb06dPVv3//u34fV2xsrGJjY233o6KiJN1aghgXF5eiY0pria/v6DqQdTBnkFLZec44S3q5YQk9VjZAQxdFaOvxy3rv532at+1vvdc6RI8U8nF0iVlSdp4zSB/MGaRUZpozKanBYhy4CP3UqVMqWLCg1q9fr1q1atm2DxgwQKtWrdKmTZuSPKZTp07atWuXFi5cqODgYIWHh6t169aKj4+3C0iJvv/+e3Xq1EknTpxQgQIFkq1j+PDhGjFiRJLtM2fOlKen5wMcIQAgs0gw0qZzFi067qRr8RZZZFQ30Khl4QRZHfqnRgBAZnbt2jV16tRJkZGR8vb2vufYLBeuzp8/r549e2rx4sWyWCwKDg5W48aNNWnSJF2/nnQtfVhYmNzc3LR48eK71pHcmavChQvrwoUL//oGpre4uDgtW7ZMTZo0kasr3a7w75gzSKmcNmcuXonVf5ce0MJdpyVJ+XO7a3CLMgoLCbjrCgfYy2lzBg+OOYOUykxzJioqSn5+fvcVrhz6tzo/Pz85Ozvr7NmzdtvPnj2rwMDAZB/j7++vhQsXKiYmRhcvXlSBAgU0cOBAlShRIsnY48eP6/fff//XLzV2d3e3fTHy7VxdXR3+YSbKTLUga2DOIKVyypwJzOOqTztW1tPVLujtBbt17OI1vTR7lx4tE6ARj5dV4byeik8w2nz0ks5Fxyggt1XVi+eVsxPB6045Zc4g7TBnkFKZYc6k5PUdGq7c3NxUpUoVhYeH64knnpAkJSQkKDw8XH379r3nY61WqwoWLKi4uDjNmzdP7dq1SzJm8uTJCggIUIsWLdKjfABAFlanpJ+W9quvL1ce1viVh7T8z3PacPiiHisfqPWHLupMVIxtbJCPVcNahahZuSAHVgwAyOwc3i2wf//+mjhxoqZOnap9+/apd+/eunr1qnr06CFJ6tq1q13Di02bNmn+/Pk6cuSI1qxZo2bNmikhIUEDBgywe96EhARNnjxZ3bp1k4sLi+kBAElZXZ3Vv8lDWvJKPVUvnlfX4+I1f/tJu2AlSWciY9R7+nYt3XPaQZUCALICh6eO9u3b6/z58xo6dKjOnDmjihUraunSpcqfP78k6cSJE3Jy+l8GjImJ0eDBg3XkyBF5eXmpefPmmjZtmnx9fe2e9/fff9eJEyf07LPPZuThAACyoJIBuTXz+Rqq8t7viryetCuUkWSRNGJxhJqEBLJEEACQLIeHK0nq27fvXZcBrly50u5+aGioIiIi/vU5mzZtKgf26gAAZDFbjv2TbLBKZCSdjozR5qOXVCs4X8YVBgDIMhy+LBAAgMzgXHTMvw+SdPj8lXSuBACQVRGuAACQFJDb+u+DJL2zOEIf/LJPF64k/W5FAEDORrgCAEBS9eJ5FeRj1b2upnJ1tuhGfIK+Xn1EdT9cTsgCANghXAEAIMnZyaJhrUIkKUnAsvz/7bMOlTSlRzVVLOyrmDhCFgDAHuEKAID/16xckMY/U1mBPvZLBAN9rBr/TGU9Vj5IDUoHaMF/aicbst7/OULnowlZAJBTZYpugQAAZBbNygWpSUigNh+9pHPRMQrIbVX14nnt2q9bLBY1KB2g0If8terAeX36+0Ht/OuyJq45qmkbj6tLzaJ6oX6w/HO7O/BIAAAZjXAFAMAdnJ0s99VunZAFALgdywIBAHhAiSHrzuWCE9ccVb1RLBcEgJyCcAUAQBohZAFAzka4AgAgjd0esqY+W52QBQA5BOEKAIB0YrFYFPqQ/11D1ns/RehcdIyjywQApBHCFQAA6exuIeubtUdVf9QKQhYAZBOEKwAAMgghCwCyN8IVAAAZ7M6QVakIIQsAsgPCFQAADpIYsub3JmQBQHZAuAIAwMEIWQCQPRCuAADIJG4PWd/dEbLqfbhC7xKyACBTI1wBAJDJWCwW1b8jZMXeTNC3hCwAyNQIVwAAZFKELADIWghXAABkcneGrMqELADIlAhXAABkEYkhax4hCwAyJcIVAABZDCELADInwhUAAFnU7SFr2nNJQ9Y7iyN0LoqQBQAZhXAFAEAWZ7FYVK9U0pA1ad1R1RtFyAKAjEK4AgAgmyBkAYBjEa4AAMhm7gxZVYrmIWQBQAYgXAEAkE0lhqwfetUiZAFABiBcAQCQzRGyACBjEK4AAMghbg9Z05+rQcgCgDRGuAIAIIexWCyqW8rvriFrxOK9hCwASAXCFQAAOdTdQtbkdccIWQCQCoQrAAByOEIWAKQNF0cXAAAAMofEkFWnZD6tO3RRn/5+QFuP/6PJ645p5qYT6lSjiHqHBiuPh7OjSwWATIlwBQAA7PxbyOpQrZCCbzi6SgDIfAhXAAAgWXcLWVM3nJCrxVmH3f5Un4alFOBtdXSpAJApcM0VAAC4p8SQNbdXLc14voaqFPFVnLFo6oYTqjtqhYYv2quzXJMFAIQrAABwfywWi+qU9NOs56vpPyHxqlLEVzduJmjK+luNLwhZAHI6whUAAEgRi8Wi0j5Gs56vphnP11DVonkIWQAgrrkCAACplHgmq3ZwPq0/fOuarC3H/tGU9cc0c/MJdapeRL0bBCs/12QByCEIVwAA4IEQsgDgFsIVAABIE4QsADkd4QoAAKSp20PWhsMX9UkyIatXaLACfQhZALIXwhUAAEgXFotFtUv6qRYhC0AOQbgCAADpipAFIKcgXAEAgAxxZ8j69PeD2nzsEiELQLZBuAIAABmKkAUguyJcAQAAhyBkAchuCFcAAMCh7ELWkYv6dJl9yOpYrbB6NyhJyAKQ6RGuAABApmCxWFQ72E+1StiHrKkbjmvW5r/UsTohC0DmRrgCAACZCiELQFZFuAIAAJlSkpD1+0FtPkrIApB5Ea4AAECmRsgCkFUQrgAAQJZAyAKQ2RGuAABAlvJvIatD9cLq3SBYQT4eji4VQA5DuAIAAFlSYsiqHeynDYcv6pPfD2jz0Uv6bsNxzSZkAXAAJ0cXAAAA8KBqBefT9y/W0qyeNVW9eF7diE/QdxuOK3TUSg39cY9OR153dIkAcgDCFQAAyDYIWQAciXAFAACyndtDVg1CFoAMQrgCAADZVq3gfJpDyAKQQQhXAAAg27tXyBqycI9OXSZkAXhwhCsAAJBj3B6yapa4FbKmbTyuBh8RsgA8OMIVAADIcWoF59PsFwhZANIW4QoAAORYhCwAaYlwBQAAcjxCFoC0QLgCAAD4f4QsAA+CcAUAAHCHe4WswQt3E7IAJItwBQAAcBeJIWv2CzVVq0Q+3YhP0PSNJxT60QpCFoAkCFcAAAD/omaJfJr1Qk1byIqLN4QsAEkQrgAAAO4TIQvAvRCuAAAAUoiQBSA5hCsAAIBUImQBuB3hCgAA4AHdK2S9vWC3ThKygByBcAUAAJBGkgtZMzadUANCFpAjEK4AAADSWGLImvNCTdUOJmQBOQXhCgAAIJ3UKJFPM3sSsoCcgnAFAACQzghZQM5AuAIAAMgghCwgeyNcAQAAZLB7hay3FuzW3/9cc3SJAFKBcAUAAOAgyYWsmZtOqOHHKwlZQBZEuAIAAHCwxJD1/Yu1VKckIQvIqghXAAAAmUT14nk143lCFpBVEa4AAAAyGUIWkDURrgAAADIpQhaQtRCuAAAAMrl7haxB8wlZQGZBuAIAAMgikgtZszYTsoDMgnAFAACQxSSGrLm9aqluST9CFpBJODxcffHFFypWrJisVqtq1KihzZs333VsXFyc3nnnHQUHB8tqtapChQpaunRpknEnT57UM888o3z58snDw0Ply5fX1q1b0/MwAAAAMly1Ynk1/fkahCwgk3BouJozZ4769++vYcOGafv27apQoYLCwsJ07ty5ZMcPHjxYX331lcaNG6eIiAj16tVLbdq00Y4dO2xj/vnnH9WpU0eurq5asmSJIiIiNHr0aOXJkyejDgsAACBD3S1kNfiIkAVkJIeGqzFjxqhnz57q0aOHQkJCNGHCBHl6emrSpEnJjp82bZreeustNW/eXCVKlFDv3r3VvHlzjR492jbmww8/VOHChTV58mRVr15dxYsXV9OmTRUcHJxRhwUAAOAQd4asmwmELCAjuTjqhW/cuKFt27Zp0KBBtm1OTk5q3LixNmzYkOxjYmNjZbVa7bZ5eHho7dq1tvuLFi1SWFiYnn76aa1atUoFCxbUf/7zH/Xs2fOutcTGxio2NtZ2PyoqStKtZYhxcXGpOr60kvj6jq4DWQdzBinFnEFKMWcyv4oFc2tyt8raevwfjVtxWOsPX9KszSc0d+tfalu5gHrVL6FCeTwyrB7mDFIqM82ZlNRgMcaYdKzlrk6dOqWCBQtq/fr1qlWrlm37gAEDtGrVKm3atCnJYzp16qRdu3Zp4cKFCg4OVnh4uFq3bq34+HhbOEoMX/3799fTTz+tLVu26JVXXtGECRPUrVu3ZGsZPny4RowYkWT7zJkz5enpmRaHCwAA4DCHo6SlfzvpQOStRUtOFqMa/kZNCiYon/VfHgzkcNeuXVOnTp0UGRkpb2/ve47NUuHq/Pnz6tmzpxYvXiyLxaLg4GA1btxYkyZN0vXr1yVJbm5uqlq1qtavX2973Msvv6wtW7bc84zYnWeuChcurAsXLvzrG5je4uLitGzZMjVp0kSurq4OrQVZA3MGKcWcQUoxZ7Kubcf/0bgVR7Tu8EVJkouTJUPOZDFnkFKZac5ERUXJz8/vvsKVw5YF+vn5ydnZWWfPnrXbfvbsWQUGBib7GH9/fy1cuFAxMTG6ePGiChQooIEDB6pEiRK2MUFBQQoJCbF73MMPP6x58+bdtRZ3d3e5u7sn2e7q6urwDzNRZqoFWQNzBinFnEFKMWeynpolA1SzZIC2HrukseEHtebgBc3ZelLztp/S01UL6T8NSqpw3vRbtcOcQUplhjmTktd3WEMLNzc3ValSReHh4bZtCQkJCg8PtzuTlRyr1aqCBQvq5s2bmjdvnlq3bm3bV6dOHe3fv99u/IEDB1S0aNG0PQAAAIAsqmqxvJr2XA390KuW6pVKbHzx1/+3cP9Df12i8QWQGg7tFti/f39NnDhRU6dO1b59+9S7d29dvXpVPXr0kCR17drVruHFpk2bNH/+fB05ckRr1qxRs2bNlJCQoAEDBtjGvPrqq9q4caM++OADHTp0SDNnztTXX3+tPn36ZPjxAQAAZGaELCBtOWxZoCS1b99e58+f19ChQ3XmzBlVrFhRS5cuVf78+SVJJ06ckJPT//JfTEyMBg8erCNHjsjLy0vNmzfXtGnT5OvraxtTrVo1LViwQIMGDdI777yj4sWL69NPP1Xnzp0z+vAAAACyhMSQdftywVmb/9LcrX9nyHJBILtwaLiSpL59+6pv377J7lu5cqXd/dDQUEVERPzrc7Zs2VItW7ZMi/IAAAByjHuFrKeqFFKfhoQs4F4cuiwQAAAAmU9yywVnb7m1XHDgPJYLAndDuAIAAECyEkPWvN6ELOB+EK4AAABwT1WKErKA+0G4AgAAwH0hZAH3RrgCAABAihCygOQRrgAAAJAq9wpZb/5AyELOQ7gCAADAA0kuZM3ZelvI+oeQhZyBcAUAAIA08b+QVVv1H/K3haymn67TrMNOhCxke4QrAAAApKkqRfPou2er24Wsjeec1PTTdXrzhz904iIhC9kT4QoAAADpIjFkfd+zusr4JNjOZD06eiUhC9kS4QoAAADpqlIRX/UOSdD3PavbLRckZCG7IVwBAAAgQ1Qq4ptkueCcrX+p4eiVGvDDLkIWsjzCFQAAADLUnddkxScYfb/1b0IWsjzCFQAAABwiMWTN/09thRKykA0QrgAAAOBQlYvk0VRCFrIBwhUAAAAyBUIWsjrCFQAAADIVQhayKsIVAAAAMqV7haw35u7S8YtXHV0iYIdwBQAAgEwtuZA1d9vfenT0KkIWMhXCFQAAALIEQhYyO8IVAAAAspTEkLXgP7XVoDQhC5kH4QoAAABZUqUieTSlByELmQfhCgAAAFkaIQuZBeEKAAAA2cK9Qtbrc3fp2AVCFtIX4QoAAADZSnIh64dtf6vRGEIW0hfhCgAAANkSIQsZjXAFAACAbC0xZC3sU0cNCVlIR4QrAAAA5AgVC/tqMiEL6YhwBQAAgByFkIX0QrgCAABAjnSvkPXa94QspBzhCgAAADlaciFr3nZCFlKOcAUAAACIkIUHR7gCAAAAbnN7yHq0TAAhC/eNcAUAAAAko2JhX03qXk0/ErJwnwhXAAAAwD1UIGThPhGuAAAAgPtwt5D16OiV6v/9Th0lZOV4hCsAAAAgBe4MWQlGmr/9pBoRsnI8whUAAACQCoQs3IlwBQAAADwAQhYSEa4AAACANHB7yGpEyMqRCFcAAABAGqpQ2Fffdq+mRX0JWTkN4QoAAABIB48UukfImrNTR85fcXSJSGOEKwAAACAdJRuydpxU4zGrCFnZDOEKAAAAyACErOyPcAUAAABkIEJW9kW4AgAAABzg9pDV+GFCVnZAuAIAAAAc6JFCvvqmWzUt7luXkJXFEa4AAACATKB8IZ+7hqxX5+zUYUJWpke4AgAAADKR5ELWgh0n1YSQlekRrgAAAIBMiJCV9RCuAAAAgEyMkJV1EK4AAACALMA+ZOUnZGVChCsAAAAgC7kVsqoSsjIhwhUAAACQBd0rZPWbvYOQ5QCEKwAAACALSy5kLdx5ipDlAIQrAAAAIBtIDFk/vUTIchTCFQAAAJCNlCt475B16BwhK70QrgAAAIBs6G4hq+knhKz0QrgCAAAAsrHbQ1aTEEJWeiJcAQAAADlAuYI+mtg1achq8skqvULIShOEKwAAACAHuTNkGSP9SMhKE4QrAAAAIAciZKU9whUAAACQgxGy0g7hCgAAAAAhKw0QrgAAAADY3B6ymhKyUoRwBQAAACCJcgV99DUhK0UIVwAAAADu6l4h6+VZO3ToXLSjS8w0CFcAAAAA/lVyIWvRrlNq8slqQtb/I1wBAAAAuG+ErLsjXAEAAABIMUJWUoQrAAAAAKmWGLJ+frmuwsrm7JBFuAIAAADwwMoW8NFXXXJ2yCJcAQAAAEgz9wpZL83aoYNns2/IIlwBAAAASHPJhazFu06p6afZN2QRrgAAAACkm5SGrPgEo01HL2nbBYs2Hb2k+ATjoMpTzsXRBQAAAADI/hJD1t5Tkfos/KB+3XtWi3ed0k9/nFLLRwro5UdL6vD5KxqxOEKnI2MkOeu7g1sV5GPVsFYhalYuyNGH8K8IVwAAAAAyTGLIijgVpc/CD2rp3jNavOuUFu86lez4M5Ex6j19u8Y/UznTByyWBQIAAADIcCEFvDWhSxX98nI9hYXkv+u4xEWBIxZHZPolgoQrAAAAAA4TUsBb3esUv+cYI+l0ZIw2H72UMUWlEuEKAAAAgEOdi45J03GOQrgCAAAA4FABua1pOs5RCFcAAAAAHKp68bwK8rHKcpf9FklBPlZVL543I8tKMcIVAAAAAIdydrJoWKsQSUoSsBLvD2sVImenu8WvzIFwBQAAAMDhmpUL0vhnKivQx37pX6CPNUu0YZf4nisAAAAAmUSzckFqEhKoDYfO6bc1m9S0Xg3VKhmQ6c9YJSJcAQAAAMg0nJ0sqlE8ry7uM6pRPG+WCVZSJlkW+MUXX6hYsWKyWq2qUaOGNm/efNexcXFxeueddxQcHCyr1aoKFSpo6dKldmOGDx8ui8VidytTpkx6HwYAAACAHMzh4WrOnDnq37+/hg0bpu3bt6tChQoKCwvTuXPnkh0/ePBgffXVVxo3bpwiIiLUq1cvtWnTRjt27LAbV7ZsWZ0+fdp2W7t2bUYcDgAAAIAcyuHhasyYMerZs6d69OihkJAQTZgwQZ6enpo0aVKy46dNm6a33npLzZs3V4kSJdS7d281b95co0ePthvn4uKiwMBA283Pzy8jDgcAAABADuXQa65u3Lihbdu2adCgQbZtTk5Oaty4sTZs2JDsY2JjY2W12ncQ8fDwSHJm6uDBgypQoICsVqtq1aqlkSNHqkiRInd9ztjYWNv9qKgoSbeWIMbFxaXq2NJK4us7ug5kHcwZpBRzBinFnEFKMWeQUplpzqSkBosxxqRjLfd06tQpFSxYUOvXr1etWrVs2wcMGKBVq1Zp06ZNSR7TqVMn7dq1SwsXLlRwcLDCw8PVunVrxcfH2wLSkiVLdOXKFZUuXVqnT5/WiBEjdPLkSe3Zs0e5c+dO8pzDhw/XiBEjkmyfOXOmPD090/CIAQAAAGQl165dU6dOnRQZGSlvb+97js1y4er8+fPq2bOnFi9eLIvFouDgYDVu3FiTJk3S9evXk32dy5cvq2jRohozZoyee+65JPuTO3NVuHBhXbhw4V/fwPQWFxenZcuWqUmTJnJ1dXVoLcgamDNIKeYMUoo5g5RiziClMtOciYqKkp+f332FK4cuC/Tz85Ozs7POnj1rt/3s2bMKDAxM9jH+/v5auHChYmJidPHiRRUoUEADBw5UiRIl7vo6vr6+euihh3To0KFk97u7u8vd3T3JdldXV4d/mIkyUy3IGpgzSCnmDFKKOYOUYs4gpTLDnEnJ6zu0oYWbm5uqVKmi8PBw27aEhASFh4fbnclKjtVqVcGCBXXz5k3NmzdPrVu3vuvYK1eu6PDhwwoKyvzf6gwAAAAga3J4t8D+/ftr4sSJmjp1qvbt26fevXvr6tWr6tGjhySpa9eudg0vNm3apPnz5+vIkSNas2aNmjVrpoSEBA0YMMA25vXXX9eqVat07NgxrV+/Xm3atJGzs7M6duyY4ccHAAAAIGdw6LJASWrfvr3Onz+voUOH6syZM6pYsaKWLl2q/PnzS5JOnDghJ6f/ZcCYmBgNHjxYR44ckZeXl5o3b65p06bJ19fXNubvv/9Wx44ddfHiRfn7+6tu3brauHGj/P39M/rwAAAAAOQQDg9XktS3b1/17ds32X0rV660ux8aGqqIiIh7Pt/s2bPTqjQAAAAAuC8OXxYIAAAAANkB4QoAAAAA0kCmWBaY2SR+9VdUVJSDK7nV4//atWuKiopyeBtKZA3MGaQUcwYpxZxBSjFnkFKZac4kZoL7+XpgwlUyoqOjJUmFCxd2cCUAAAAAMoPo6Gj5+Pjcc4zF3E8Ey2ESEhJ06tQp5c6dWxaLxaG1REVFqXDhwvrrr7/+9RuhAYk5g5RjziClmDNIKeYMUiozzRljjKKjo1WgQAG7LubJ4cxVMpycnFSoUCFHl2HH29vb4RMLWQtzBinFnEFKMWeQUswZpFRmmTP/dsYqEQ0tAAAAACANEK4AAAAAIA0QrjI5d3d3DRs2TO7u7o4uBVkEcwYpxZxBSjFnkFLMGaRUVp0zNLQAAAAAgDTAmSsAAAAASAOEKwAAAABIA4QrAAAAAEgDhCsAAAAASAOEq0zsiy++ULFixWS1WlWjRg1t3rzZ0SUhA4wcOVLVqlVT7ty5FRAQoCeeeEL79++3GxMTE6M+ffooX7588vLyUtu2bXX27Fm7MSdOnFCLFi3k6empgIAAvfHGG7p586bdmJUrV6py5cpyd3dXyZIlNWXKlPQ+PGSA//73v7JYLOrXr59tG3MGdzp58qSeeeYZ5cuXTx4eHipfvry2bt1q22+M0dChQxUUFCQPDw81btxYBw8etHuOS5cuqXPnzvL29pavr6+ee+45XblyxW7MH3/8oXr16slqtapw4cIaNWpUhhwf0lZ8fLyGDBmi4sWLy8PDQ8HBwXr33Xd1e1805gxWr16tVq1aqUCBArJYLFq4cKHd/oycI3PnzlWZMmVktVpVvnx5/fLLL2l+vMkyyJRmz55t3NzczKRJk8zevXtNz549ja+vrzl79qyjS0M6CwsLM5MnTzZ79uwxO3fuNM2bNzdFihQxV65csY3p1auXKVy4sAkPDzdbt241NWvWNLVr17btv3nzpilXrpxp3Lix2bFjh/nll1+Mn5+fGTRokG3MkSNHjKenp+nfv7+JiIgw48aNM87Ozmbp0qUZerxIW5s3bzbFihUzjzzyiHnllVds25kzuN2lS5dM0aJFTffu3c2mTZvMkSNHzK+//moOHTpkG/Pf//7X+Pj4mIULF5pdu3aZxx9/3BQvXtxcv37dNqZZs2amQoUKZuPGjWbNmjWmZMmSpmPHjrb9kZGRJn/+/KZz585mz549ZtasWcbDw8N89dVXGXq8eHDvv/++yZcvn/npp5/M0aNHzdy5c42Xl5cZO3asbQxzBr/88ot5++23zfz5840ks2DBArv9GTVH1q1bZ5ydnc2oUaNMRESEGTx4sHF1dTW7d+9O9/eAcJVJVa9e3fTp08d2Pz4+3hQoUMCMHDnSgVXBEc6dO2ckmVWrVhljjLl8+bJxdXU1c+fOtY3Zt2+fkWQ2bNhgjLn1w83JycmcOXPGNmb8+PHG29vbxMbGGmOMGTBggClbtqzda7Vv396EhYWl9yEhnURHR5tSpUqZZcuWmdDQUFu4Ys7gTm+++aapW7fuXfcnJCSYwMBA89FHH9m2Xb582bi7u5tZs2YZY4yJiIgwksyWLVtsY5YsWWIsFos5efKkMcaYL7/80uTJk8c2hxJfu3Tp0ml9SEhnLVq0MM8++6zdtieffNJ07tzZGMOcQVJ3hquMnCPt2rUzLVq0sKunRo0a5sUXX0zTY0wOywIzoRs3bmjbtm1q3LixbZuTk5MaN26sDRs2OLAyOEJkZKQkKW/evJKkbdu2KS4uzm5+lClTRkWKFLHNjw0bNqh8+fLKnz+/bUxYWJiioqK0d+9e25jbnyNxDHMs6+rTp49atGiR5HNlzuBOixYtUtWqVfX0008rICBAlSpV0sSJE237jx49qjNnzth93j4+PqpRo4bdnPH19VXVqlVtYxo3biwnJydt2rTJNqZ+/fpyc3OzjQkLC9P+/fv1zz//pPdhIg3Vrl1b4eHhOnDggCRp165dWrt2rR577DFJzBn8u4ycI478/xXhKhO6cOGC4uPj7X7JkaT8+fPrzJkzDqoKjpCQkKB+/fqpTp06KleunCTpzJkzcnNzk6+vr93Y2+fHmTNnkp0/ifvuNSYqKkrXr19Pj8NBOpo9e7a2b9+ukSNHJtnHnMGdjhw5ovHjx6tUqVL69ddf1bt3b7388suaOnWqpP995vf6/9CZM2cUEBBgt9/FxUV58+ZN0bxC1jBw4EB16NBBZcqUkaurqypVqqR+/fqpc+fOkpgz+HcZOUfuNiYj5pBLur8CgFTr06eP9uzZo7Vr1zq6FGRif/31l1555RUtW7ZMVqvV0eUgC0hISFDVqlX1wQcfSJIqVaqkPXv2aMKECerWrZuDq0Nm9P3332vGjBmaOXOmypYtq507d6pfv34qUKAAcwa4DWeuMiE/Pz85Ozsn6eR19uxZBQYGOqgqZLS+ffvqp59+0ooVK1SoUCHb9sDAQN24cUOXL1+2G3/7/AgMDEx2/iTuu9cYb29veXh4pPXhIB1t27ZN586dU+XKleXi4iIXFxetWrVKn332mVxcXJQ/f37mDOwEBQUpJCTEbtvDDz+sEydOSPrfZ36v/w8FBgbq3Llzdvtv3rypS5cupWheIWt44403bGevypcvry5duujVV1+1nS1nzuDfZOQcuduYjJhDhKtMyM3NTVWqVFF4eLhtW0JCgsLDw1WrVi0HVoaMYIxR3759tWDBAi1fvlzFixe321+lShW5urrazY/9+/frxIkTtvlRq1Yt7d692+4H1LJly+Tt7W37hapWrVp2z5E4hjmW9TRq1Ei7d+/Wzp07bbeqVauqc+fOtv9mzuB2derUSfIVDwcOHFDRokUlScWLF1dgYKDd5x0VFaVNmzbZzZnLly9r27ZttjHLly9XQkKCatSoYRuzevVqxcXF2cYsW7ZMpUuXVp48edLt+JD2rl27Jicn+18bnZ2dlZCQIIk5g3+XkXPEof+/SveWGUiV2bNnG3d3dzNlyhQTERFhXnjhBePr62vXyQvZU+/evY2Pj49ZuXKlOX36tO127do125hevXqZIkWKmOXLl5utW7eaWrVqmVq1atn2J7bVbtq0qdm5c6dZunSp8ff3T7at9htvvGH27dtnvvjiC9pqZyO3dws0hjkDe5s3bzYuLi7m/fffNwcPHjQzZswwnp6eZvr06bYx//3vf42vr6/58ccfzR9//GFat26dbMvkSpUqmU2bNpm1a9eaUqVK2bVMvnz5ssmfP7/p0qWL2bNnj5k9e7bx9PSkrXYW1K1bN1OwYEFbK/b58+cbPz8/M2DAANsY5gyio6PNjh07zI4dO4wkM2bMGLNjxw5z/PhxY0zGzZF169YZFxcX8/HHH5t9+/aZYcOG0YodxowbN84UKVLEuLm5merVq5uNGzc6uiRkAEnJ3iZPnmwbc/36dfOf//zH5MmTx3h6epo2bdqY06dP2z3PsWPHzGOPPWY8PDyMn5+fee2110xcXJzdmBUrVpiKFSsaNzc3U6JECbvXQNZ2Z7hizuBOixcvNuXKlTPu7u6mTJky5uuvv7bbn5CQYIYMGWLy589v3N3dTaNGjcz+/fvtxly8eNF07NjReHl5GW9vb9OjRw8THR1tN2bXrl2mbt26xt3d3RQsWND897//TfdjQ9qLiooyr7zyiilSpIixWq2mRIkS5u2337Zrh82cwYoVK5L9HaZbt27GmIydI99//7156KGHjJubmylbtqz5+eef0+24b2cx5rav1gYAAAAApArXXAEAAABAGiBcAQAAAEAaIFwBAAAAQBogXAEAAABAGiBcAQAAAEAaIFwBAAAAQBogXAEAAABAGiBcAQAAAEAaIFwBAAAAQBogXAEA0sz58+fVu3dvFSlSRO7u7goMDFRYWJjWrVsnSerQoYOaNWtm95ilS5fKYrFo+PDhdtuHDx+uIkWK2G0bOXKknJ2d9dFHH91XPRaLJcmtbt26qT9AAADugXAFAEgzbdu21Y4dOzR16lQdOHBAixYtUoMGDXTx4kVJUsOGDbVu3TrdvHnT9pgVK1aocOHCWrlypd1zrVixQg0bNrTbNmnSJA0YMECTJk2675omT56s06dP226LFi1KdlxcXNx9PycAAMkhXAEA0sTly5e1Zs0affjhh2rYsKGKFi2q6tWra9CgQXr88ccl3QpXV65c0datW22PW7lypQYOHKhNmzYpJiZGkhQTE6NNmzbZhatVq1bp+vXreueddxQVFaX169ffV12+vr4KDAy03fLmzatjx47JYrFozpw5Cg0NldVq1YwZMyRJ33zzjR5++GFZrVaVKVNGX375pd3zbd68WZUqVZLValXVqlW1YMECWSwW7dy5U5I0ZcoU+fr62j1m4cKFslgsdtt+/PFHVa5cWVarVSVKlNCIESPsQqfFYtE333yjNm3ayNPTU6VKlUoSDPfu3auWLVvK29tbuXPnVr169XT48GGtXr1arq6uOnPmjN34fv36qV69evf1vgEAUo5wBQBIE15eXvLy8tLChQsVGxub7JiHHnpIBQoU0IoVKyRJ0dHR2r59u55++mkVK1ZMGzZskCStX79esbGxduHq22+/VceOHeXq6qqOHTvq22+/feCaBw4cqFdeeUX79u1TWFiYZsyYoaFDh+r999/Xvn379MEHH2jIkCGaOnWqJOnKlStq2bKlQkJCtG3bNg0fPlyvv/56il93zZo16tq1q1555RVFREToq6++0pQpU/T+++/bjRsxYoTatWunP/74Q82bN1fnzp116dIlSdLJkydVv359ubu7a/ny5dq2bZueffZZ3bx5U/Xr11eJEiU0bdo023PFxcVpxowZevbZZx/gHQMA3JMBACCN/PDDDyZPnjzGarWa2rVrm0GDBpldu3bZjencubNp2rSpMcaYn3/+2YSEhBhjjHnhhRfM0KFDjTHGDBkyxBQvXtz2mMjISOPh4WF27txpjDFmx44dxsvLy0RHR9+zHknGarWaXLly2W4LFiwwR48eNZLMp59+ajc+ODjYzJw5027bu+++a2rVqmWMMearr74y+fLlM9evX7ftHz9+vJFkduzYYYwxZvLkycbHx8fuORYsWGBu/19uo0aNzAcffGA3Ztq0aSYoKMiu9sGDB9vuX7lyxUgyS5YsMcYYM2jQIFO8eHFz48aNZI/9ww8/NA8//LDt/rx584yXl5e5cuVKsuMBAA+OM1cAgDTTtm1bnTp1SosWLVKzZs20cuVKVa5cWVOmTLGNadCggdatW6e4uDitXLlSDRo0kCSFhobarrtauXKl3VmrWbNmKTg4WBUqVJAkVaxYUUWLFtWcOXP+taZPPvlEO3futN2aNGli21e1alXbf1+9elWHDx/Wc889ZzsL5+Xlpffee0+HDx+WJO3bt0+PPPKIrFar7XG1atVK8fu0a9cuvfPOO3av07NnT50+fVrXrl2zjXvkkUds/50rVy55e3vr3LlzkqSdO3eqXr16cnV1TfY1unfvrkOHDmnjxo2Sbi1XbNeunXLlypXiegEA98fF0QUAALIXq9WqJk2aqEmTJhoyZIief/55DRs2TN27d5d067qrq1evasuWLVqxYoXeeOMNSbfC1bPPPqtLly5p06ZNevHFF23P+e2332rv3r1ycfnf/7YSEhI0adIkPffcc/esJzAwUCVLlrTbdv78eUmyCxpXrlyRJE2cOFE1atSwG+/s7Hzfx+/k5CRjjN22O5tlXLlyRSNGjNCTTz6Z5PG3B7c7g5PFYlFCQoIkycPD4551BAQEqFWrVpo8ebKKFy+uJUuWJGkaAgBIW4QrAEC6CgkJ0cKFC233g4ODVbhwYS1atEg7d+5UaGioJKlgwYIqWLCgRo8erRs3btjOXO3evVtbt27VypUrlTdvXtvzXLp0SQ0aNNCff/6pMmXKPHCd+fPnV4ECBXTkyBF17tw52TEPP/ywpk2bppiYGFsISjwzlMjf31/R0dG6evWqLbwlNrtIVLlyZe3fvz9J6EuJRx55RFOnTlVcXNxdz149//zz6tixowoVKqTg4GDVqVMn1a8HAPh3LAsEAKSJixcv6tFHH9X06dP1xx9/6OjRo5o7d65GjRql1q1b241t2LChvvzyS5UsWVL58+e3bQ8NDdW4ceNsjS+kW2etqlevrvr166tcuXK2W/369VWtWrU0aWyRaMSIERo5cqQ+++wzHThwQLt379bkyZM1ZswYSVKnTp1ksVjUs2dPRURE6JdfftHHH39s9xw1atSQp6en3nrrLR0+fFgzZ860WxYpSUOHDtV3332nESNGaO/evdq3b59mz56twYMH33etffv2VVRUlDp06KCtW7fq4MGDmjZtmvbv328bExYWJm9vb7333nvq0aNH6t8YAMB9IVwBANKEl5eXatSooU8++cQWhIYMGaKePXvq888/txvbsGFDRUdH2663ShQaGqro6GjbWasbN25o+vTpatu2bbKv2bZtW3333Xdp9h1Vzz//vL755htNnjxZ5cuXV2hoqKZMmaLixYvbjnHx4sXavXu3KlWqpLffflsffvih3XPkzZtX06dP1y+//KLy5ctr1qxZSb4gOSwsTD/99JN+++03VatWTTVr1tQnn3yiokWL3net+fLl0/Lly3XlyhWFhoaqSpUqmjhxot1ZLCcnJ3Xv3l3x8fHq2rVr6t8YAMB9sZg7F4YDAID7duzYMRUvXlw7duxQxYoVHV1OEs8995zOnz9/1y9PBgCkHa65AgAgG4qMjNTu3bs1c+ZMghUAZBDCFQAA2VDr1q21efNm9erVy679PAAg/bAsEAAAAADSAA0tAAAAACANEK4AAAAAIA0QrgAAAAAgDRCuAAAAACANEK4AAAAAIA0QrgAAAAAgDRCuAAAAACANEK4AAAAAIA38H+pi3b1ERLtIAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.utils import shuffle\n",
    "from river import compose, preprocessing, metrics, stream\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT.csv\")\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)  # Shuffle the data to ensure randomness\n",
    "\n",
    "# Define the list of SWA frequencies to experiment with\n",
    "swa_frequencies = [10, 50, 100, 200, 500, 1000, 5000, 10000]\n",
    "\n",
    "# Initialize a dictionary to store accuracy results for each SWA frequency\n",
    "recall_results = {}\n",
    "\n",
    "# Iterate over each SWA frequency to evaluate performance\n",
    "for freq in swa_frequencies:\n",
    "    print(f\"\\nEvaluating SWA Frequency: {freq}\")\n",
    "\n",
    "    # Define the pipeline with StandardScaler and ALMASWAClassifier\n",
    "    model = compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier(swa_freq=freq)\n",
    "    )\n",
    "    \n",
    "    # Initialize the accuracy metric\n",
    "    recall = metrics.Recall()\n",
    "    \n",
    "    # Stream the data and update the model and metric\n",
    "    for xi, yi_true in stream.iter_pandas(X, y):\n",
    "        y_pred = model.predict_one(xi)  # Make a prediction\n",
    "        model.learn_one(xi, yi_true)    # Update the model with the true label\n",
    "        recall.update(yi_true, y_pred)  # Update the accuracy metric\n",
    "    \n",
    "    # Store the final accuracy for this SWA frequency\n",
    "    final_recall = recall.get()\n",
    "    recall_results[freq] = final_recall\n",
    "    print(f\"Final Recall for SWA Frequency {freq}: {final_recall:.4f}\")\n",
    "\n",
    "# Identify the SWA frequency with the highest accuracy\n",
    "best_freq = max(recall_results, key=recall_results.get)\n",
    "best_recall = recall_results[best_freq]\n",
    "\n",
    "print(\"\\n=== Experiment Results ===\")\n",
    "for freq, rec in recall_results.items():\n",
    "    print(f\"SWA Frequency: {freq} | Recall: {rec:.4f}\")\n",
    "    \n",
    "print(f\"\\nOptimal SWA Frequency: {best_freq} with Recall: {best_recall:.4f}\")\n",
    "\n",
    "# Optional: Plot the results for visualization\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(list(recall_results.keys()), list(recall_results.values()), marker='o', linestyle='-')\n",
    "plt.title('ALMASWAClassifier Recall vs. SWA Frequency')\n",
    "plt.xlabel('SWA Frequency')\n",
    "plt.ylabel('Recall')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61870a0c-1ccc-4a65-ad09-cebb5b1ab732",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "112cf1ff-7ae0-4f62-9941-b8452778340c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Running Experiments: 100%|████████████████████████| 5/5 [02:06<00:00, 25.21s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9194 ± 0.0039\n",
      "Recall: 0.9301 ± 0.0041\n",
      "\n",
      "\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9818 ± 0.0005\n",
      "Recall: 0.9942 ± 0.0005\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- ALMAClassifier ---\n",
      "Accuracy CV: 0.0043\n",
      "Recall CV: 0.0044\n",
      "\n",
      "\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy CV: 0.0005\n",
      "Recall CV: 0.0005\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from river import compose, preprocessing, linear_model, metrics, stream\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "\n",
    "# Experiment Parameters\n",
    "NUM_RUNS = 5  # Number of independent runs to assess variance\n",
    "RANDOM_SEED = 42  # Base random seed for reproducibility\n",
    "METRIC_RECORD_INTERVAL = 1000  # Record metrics every 1000 samples\n",
    "\n",
    "# Initialize dictionaries to store metrics across runs\n",
    "results = {\n",
    "    'ALMAClassifier': {\n",
    "        'Accuracy': [],\n",
    "        'Recall': [],\n",
    "        'Accuracy_over_time': [],\n",
    "        'Recall_over_time': []\n",
    "    },\n",
    "    'ALMASWAClassifier': {\n",
    "        'Accuracy': [],\n",
    "        'Recall': [],\n",
    "        'Accuracy_over_time': [],\n",
    "        'Recall_over_time': []\n",
    "    }\n",
    "}\n",
    "\n",
    "def run_experiment(run_id):\n",
    "    # Set unique random state for each run for variability\n",
    "    random_state = RANDOM_SEED + run_id\n",
    "\n",
    "    # Load and prepare data\n",
    "    data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-BoT-IoT/NF-BoT-IoT.csv\")\n",
    "    X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "    y = data['Label']\n",
    "    X, y = shuffle(X, y, random_state=random_state)\n",
    "    \n",
    "    # Define classifiers with their respective pipelines\n",
    "    classifiers = {\n",
    "        'ALMAClassifier': compose.Pipeline(\n",
    "            preprocessing.StandardScaler(),\n",
    "            linear_model.ALMAClassifier()\n",
    "        ),\n",
    "        'ALMASWAClassifier': compose.Pipeline(\n",
    "            preprocessing.StandardScaler(),\n",
    "            ALMASWAClassifier()  # Ensure ALMASWAClassifier is correctly imported\n",
    "        )\n",
    "    }\n",
    "    \n",
    "    # Initialize metrics for each classifier\n",
    "    metrics_dict = {\n",
    "        name: {\n",
    "            'Accuracy': metrics.Accuracy(),\n",
    "            'Recall': metrics.Recall()\n",
    "        }\n",
    "        for name in classifiers\n",
    "    }\n",
    "    \n",
    "    # Initialize temporary storage for convergence metrics\n",
    "    temp_metrics = {\n",
    "        name: {\n",
    "            'Accuracy_over_time': [],\n",
    "            'Recall_over_time': []\n",
    "        }\n",
    "        for name in classifiers\n",
    "    }\n",
    "    \n",
    "    # Iterate through the data stream and update models and metrics\n",
    "    for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "        for name, model in classifiers.items():\n",
    "            y_pred = model.predict_one(x)\n",
    "            model.learn_one(x, true_y)\n",
    "            metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "            metrics_dict[name]['Recall'].update(true_y, y_pred)\n",
    "        \n",
    "        # Record metrics at specified intervals\n",
    "        if (i + 1) % METRIC_RECORD_INTERVAL == 0:\n",
    "            for name in classifiers:\n",
    "                temp_metrics[name]['Accuracy_over_time'].append(metrics_dict[name]['Accuracy'].get())\n",
    "                temp_metrics[name]['Recall_over_time'].append(metrics_dict[name]['Recall'].get())\n",
    "    \n",
    "    # After the stream, collect final metrics\n",
    "    for name in classifiers:\n",
    "        results[name]['Accuracy'].append(metrics_dict[name]['Accuracy'].get())\n",
    "        results[name]['Recall'].append(metrics_dict[name]['Recall'].get())\n",
    "        results[name]['Accuracy_over_time'].append(temp_metrics[name]['Accuracy_over_time'])\n",
    "        results[name]['Recall_over_time'].append(temp_metrics[name]['Recall_over_time'])\n",
    "\n",
    "# Run the experiments multiple times\n",
    "for run in tqdm(range(NUM_RUNS), desc=\"Running Experiments\"):\n",
    "    run_experiment(run)\n",
    "\n",
    "# Compute Mean and Standard Deviation for Final Metrics\n",
    "final_metrics = {}\n",
    "for name in results:\n",
    "    final_metrics[name] = {\n",
    "        'Accuracy_Mean': np.mean(results[name]['Accuracy']),\n",
    "        'Accuracy_Std': np.std(results[name]['Accuracy']),\n",
    "        'Recall_Mean': np.mean(results[name]['Recall']),\n",
    "        'Recall_Std': np.std(results[name]['Recall'])\n",
    "    }\n",
    "\n",
    "# Compute Mean and Std for Convergence Metrics\n",
    "convergence_metrics = {}\n",
    "for name in results:\n",
    "    convergence_metrics[name] = {\n",
    "        'Accuracy_over_time_mean': np.mean(results[name]['Accuracy_over_time'], axis=0),\n",
    "        'Accuracy_over_time_std': np.std(results[name]['Accuracy_over_time'], axis=0),\n",
    "        'Recall_over_time_mean': np.mean(results[name]['Recall_over_time'], axis=0),\n",
    "        'Recall_over_time_std': np.std(results[name]['Recall_over_time'], axis=0)\n",
    "    }\n",
    "\n",
    "# Display Final Aggregated Results\n",
    "for name, metrics_values in final_metrics.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metrics_values['Accuracy_Mean']:.4f} ± {metrics_values['Accuracy_Std']:.4f}\")\n",
    "    print(f\"Recall: {metrics_values['Recall_Mean']:.4f} ± {metrics_values['Recall_Std']:.4f}\")\n",
    "    print(\"\\n\")\n",
    "\n",
    "# Plot Convergence Over Time\n",
    "# Define the number of metric recording points\n",
    "num_points = len(convergence_metrics['ALMAClassifier']['Accuracy_over_time_mean'])\n",
    "\n",
    "# Define x-axis: number of samples processed at each recording point\n",
    "x_axis = [METRIC_RECORD_INTERVAL * (i + 1) for i in range(num_points)]\n",
    "\n",
    "# Plot Accuracy Over Time\n",
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "for name in convergence_metrics:\n",
    "    mean = convergence_metrics[name]['Accuracy_over_time_mean']\n",
    "    std = convergence_metrics[name]['Accuracy_over_time_std']\n",
    "    plt.plot(x_axis, mean, label=f'{name} Accuracy')\n",
    "    plt.fill_between(x_axis, np.array(mean) - np.array(std), np.array(mean) + np.array(std), alpha=0.2)\n",
    "plt.xlabel('Number of Samples Processed')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracy Over Time')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# Plot Recall Over Time\n",
    "plt.subplot(1, 2, 2)\n",
    "for name in convergence_metrics:\n",
    "    mean = convergence_metrics[name]['Recall_over_time_mean']\n",
    "    std = convergence_metrics[name]['Recall_over_time_std']\n",
    "    plt.plot(x_axis, mean, label=f'{name} Recall')\n",
    "    plt.fill_between(x_axis, np.array(mean) - np.array(std), np.array(mean) + np.array(std), alpha=0.2)\n",
    "plt.xlabel('Number of Samples Processed')\n",
    "plt.ylabel('Recall')\n",
    "plt.title('Recall Over Time')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Compute Coefficient of Variation (CV) for Final Metrics\n",
    "for name, metrics_values in final_metrics.items():\n",
    "    accuracy_cv = metrics_values['Accuracy_Std'] / metrics_values['Accuracy_Mean']\n",
    "    recall_cv = metrics_values['Recall_Std'] / metrics_values['Recall_Mean']\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy CV: {accuracy_cv:.4f}\")\n",
    "    print(f\"Recall CV: {recall_cv:.4f}\")\n",
    "    print(\"\\n\")\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "146e8800-5075-4e37-a45f-4507626b25f8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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