AutoML_Online_Learning_Dataset_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial: Automated Machine Learning\n",
    "This is the code for the paper entitled \"**[IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://arxiv.org/abs/2209.08018)**\" published in *Engineering Applications of Artificial Intelligence* (Elsevier's Journal, IF:7.8).<br>\n",
    "Authors: Li Yang (lyang339@uwo.ca) and Abdallah Shami (Abdallah.Shami@uwo.ca)<br>\n",
    "Organization: The Optimized Computing and Communications (OC2) Lab, ECE Department, Western University\n",
    "\n",
    "L. Yang and A. Shami, \"IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective\", *Engineering Applications of Artificial Intelligence*, vol. 116, pp. 1-33, 2022, doi: https://doi.org/10.1016/j.engappai.2022.105366."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code Part 2: Automated Online Learning (Dynamic Data Stream Analytics)\n",
    "Online learning: Online learning techniques are able to train models using continuously incoming online data streams in dynamic environments. By learning a single data sample at a time, online learning models can reduce memory requirements for data storage and learn new data patterns. Additionally, online learning models can often achieve real-time processing and address concept drift issues. Thus, when applied to dynamic data streams or when inadequate data is available, online learning is often more effective than batch learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset 2: IoTID20\n",
    "A subset of the IoT network traffic data randomly sampled from the [IoTID20 dataset](https://sites.google.com/view/iot-network-intrusion-dataset/home).   \n",
    "\n",
    "IoTID20 dataset was created by using normal and attack virtual machines as network platforms, simulating IoT services with the node-red tool, and extracting features with the Information Security Center of Excellence (ISCX) flow meter program. A typical smart home environment was established for generating this dataset using five IoT devices or services: a smart fridge, a smart thermostat, motion-activated lights, a weather station, and a remotely-activated garage door. Thus, the traffic data samples of normal and abnormal IoT devices are collected in Pcap files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score, precision_score, recall_score, f1_score\n",
    "import lightgbm as lgb\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from scipy.stats import shapiro\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the sampled IoTID20 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Data/IoT_2020_b_0.01.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "      Flow_ID  Src_IP  Src_Port  Dst_IP  Dst_Port  Timestamp  Flow_Duration  \\\n",
       "0       12708   25886     53190     200      9020       3760            124   \n",
       "1          60   25883     60357     110      7760       3649          46189   \n",
       "2       12695   25883      9020     203     52739       2261             73   \n",
       "3       12690   25886     52717     200      9020       1963            149   \n",
       "4       12690   25886     52717     200      9020       1905             83   \n",
       "...       ...     ...       ...     ...       ...        ...            ...   \n",
       "6247    12659   25886     10125     200      9020       1057             70   \n",
       "6248    13708   25883     60158     233      8899       3264             40   \n",
       "6249     7655   15614      8487     200       554       3697           1283   \n",
       "6250    62696   25889     64783     233      9988       3192            222   \n",
       "6251    47156       7       443     205     43238       3299            184   \n",
       "\n",
       "      TotLen_Fwd_Pkts  TotLen_Bwd_Pkts  Bwd_Pkt_Len_Max  ...  Fwd_Pkts/s  \\\n",
       "0                 0.0           2776.0           1388.0  ...        0.00   \n",
       "1              2528.0              0.0              0.0  ...       43.30   \n",
       "2              1388.0           1388.0           1388.0  ...    13698.63   \n",
       "3                 0.0              0.0              0.0  ...    13422.82   \n",
       "4                 0.0              0.0              0.0  ...    12048.19   \n",
       "...               ...              ...              ...  ...         ...   \n",
       "6247              0.0           2776.0           1388.0  ...        0.00   \n",
       "6248            128.0             32.0             32.0  ...   100000.00   \n",
       "6249              0.0              0.0              0.0  ...        0.00   \n",
       "6250             96.0             32.0             32.0  ...    13513.51   \n",
       "6251           1441.0           1441.0           1441.0  ...     5434.78   \n",
       "\n",
       "      Bwd_Pkts/s  Pkt_Len_Std  FIN_Flag_Cnt  Pkt_Size_Avg  Init_Bwd_Win_Byts  \\\n",
       "0       16129.03         0.00             0        2082.0               1869   \n",
       "1          43.30       698.97             0         632.0                126   \n",
       "2       13698.63         0.00             0        2082.0               1869   \n",
       "3        6711.41         0.00             0           0.0              32679   \n",
       "4       12048.19         0.00             0           0.0              32422   \n",
       "...          ...          ...           ...           ...                ...   \n",
       "6247    28571.43         0.00             0        2082.0               1869   \n",
       "6248    25000.00         0.00             0          38.4                 -1   \n",
       "6249     1558.85         0.00             0           0.0              14600   \n",
       "6250     4504.50         0.00             0          40.0                 -1   \n",
       "6251     5434.78         0.00             0        2161.5                252   \n",
       "\n",
       "      Idle_Mean  Idle_Max  Idle_Min  Label  \n",
       "0        124.00     124.0     124.0      1  \n",
       "1      15396.33   40225.0    1910.0      1  \n",
       "2         73.00      73.0      73.0      1  \n",
       "3         74.50      75.0      74.0      1  \n",
       "4         83.00      83.0      83.0      1  \n",
       "...         ...       ...       ...    ...  \n",
       "6247      70.00      70.0      70.0      1  \n",
       "6248      10.00      21.0       5.0      1  \n",
       "6249    1283.00    1283.0    1283.0      1  \n",
       "6250      74.00     206.0       8.0      1  \n",
       "6251     184.00     184.0     184.0      1  \n",
       "\n",
       "[6252 rows x 32 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Automated Data Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automated Transformation/Encoding\n",
    "Automatically identify and transform string/text features into numerical features to make the data more readable by ML models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the automated data encoding function\n",
    "def Auto_Encoding(df):\n",
    "    cat_features=[x for x in df.columns if df[x].dtype==\"object\"] ## Find string/text features\n",
    "    le=LabelEncoder()\n",
    "    for col in cat_features:\n",
    "        if col in df.columns:\n",
    "            i = df.columns.get_loc(col)\n",
    "            # Transform to numerical features\n",
    "            df.iloc[:,i] = df.apply(lambda i:le.fit_transform(i.astype(str)), axis=0, result_type='expand')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=Auto_Encoding(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automated Imputation\n",
    "Detect and impute missing values to improve data quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the automated data imputation function\n",
    "def Auto_Imputation(df):\n",
    "    if df.isnull().values.any() or np.isinf(df).values.any(): # if there is any empty or infinite values\n",
    "        df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "        df.fillna(0, inplace = True)  # Replace empty values with zeros; there are other imputation methods discussed in the paper\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=Auto_Imputation(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automated normalization\n",
    "Normalize the range of features to a similar scale to improve data quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def Auto_Normalization(df):\n",
    "    stat, p = shapiro(df)\n",
    "    print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "    # interpret\n",
    "    alpha = 0.05\n",
    "    numeric_features = df.drop(['Label'],axis = 1).dtypes[df.dtypes != 'object'].index\n",
    "    \n",
    "    # check if the data distribution follows a Gaussian/normal distribution\n",
    "    # If so, select the Z-score normalization method; otherwise, select the min-max normalization\n",
    "    # Details are in the paper\n",
    "    if p > alpha:\n",
    "        print('Sample looks Gaussian (fail to reject H0)')\n",
    "        df[numeric_features] = df[numeric_features].apply(\n",
    "            lambda x: (x - x.mean()) / (x.std()))\n",
    "        print('Z-score normalization is automatically chosen and used')\n",
    "    else:\n",
    "        print('Sample does not look Gaussian (reject H0)')\n",
    "        df[numeric_features] = df[numeric_features].apply(\n",
    "            lambda x: (x - x.min()) / (x.max()-x.min()))\n",
    "        print('Min-max normalization is automatically chosen and used')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=0.108, p=0.000\n",
      "Sample does not look Gaussian (reject H0)\n",
      "Min-max normalization is automatically chosen and used\n"
     ]
    }
   ],
   "source": [
    "df=Auto_Normalization(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Automated Feature Engineering\n",
    "Feature selection method 1: **Information Gain (IG)**, used to remove irrelevant features to improve model efficiency  \n",
    "Feature selection method 2: **Pearson Correlation**, used to remove redundant features to improve model efficiency and accuracy  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove irrelevant features and select important features\n",
    "def Feature_Importance_IG(data):\n",
    "    features = data.drop(['Label'],axis=1).values  # \"Label\" should be changed to the target class variable name if different\n",
    "    labels = data['Label'].values\n",
    "    \n",
    "    # Extract feature names\n",
    "    feature_names = list(data.drop(['Label'],axis=1).columns)\n",
    "\n",
    "    # Empty array for feature importances\n",
    "    feature_importance_values = np.zeros(len(feature_names))\n",
    "    model = lgb.LGBMRegressor(verbose = -1)\n",
    "    model.fit(features, labels)\n",
    "    feature_importances = pd.DataFrame({'feature': feature_names, 'importance': model.feature_importances_})\n",
    "\n",
    "    # Sort features according to importance\n",
    "    feature_importances = feature_importances.sort_values('importance', ascending = False).reset_index(drop = True)\n",
    "\n",
    "    # Normalize the feature importances to add up to one\n",
    "    feature_importances['normalized_importance'] = feature_importances['importance'] / feature_importances['importance'].sum()\n",
    "    feature_importances['cumulative_importance'] = np.cumsum(feature_importances['normalized_importance'])\n",
    "    \n",
    "    cumulative_importance=0.90 # Only keep the important features with cumulative importance scores>=90%. It can be changed.\n",
    "\n",
    "    # Make sure most important features are on top\n",
    "    feature_importances = feature_importances.sort_values('cumulative_importance')\n",
    "\n",
    "    # Identify the features not needed to reach the cumulative_importance\n",
    "    record_low_importance = feature_importances[feature_importances['cumulative_importance'] > cumulative_importance]\n",
    "\n",
    "    to_drop = list(record_low_importance['feature'])\n",
    "#     print(feature_importances.drop(['importance'],axis=1))\n",
    "    return to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove redundant features\n",
    "def Feature_Redundancy_Pearson(data):\n",
    "    correlation_threshold=0.90 # Only remove features with the redundancy>90%. It can be changed\n",
    "    features = data.drop(['Label'],axis=1)\n",
    "    corr_matrix = features.corr()\n",
    "\n",
    "    # Extract the upper triangle of the correlation matrix\n",
    "    upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k = 1).astype(np.bool))\n",
    "\n",
    "    # Select the features with correlations above the threshold\n",
    "    # Need to use the absolute value\n",
    "    to_drop = [column for column in upper.columns if any(upper[column].abs() > correlation_threshold)]\n",
    "\n",
    "    # Dataframe to hold correlated pairs\n",
    "    record_collinear = pd.DataFrame(columns = ['drop_feature', 'corr_feature', 'corr_value'])\n",
    "\n",
    "    # Iterate through the columns to drop\n",
    "    for column in to_drop:\n",
    "\n",
    "        # Find the correlated features\n",
    "        corr_features = list(upper.index[upper[column].abs() > correlation_threshold])\n",
    "\n",
    "        # Find the correlated values\n",
    "        corr_values = list(upper[column][upper[column].abs() > correlation_threshold])\n",
    "        drop_features = [column for _ in range(len(corr_features))]    \n",
    "\n",
    "        # Record the information (need a temp df for now)\n",
    "        temp_df = pd.DataFrame.from_dict({'drop_feature': drop_features,\n",
    "                                         'corr_feature': corr_features,\n",
    "                                         'corr_value': corr_values})\n",
    "        record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
    "#     print(record_collinear)\n",
    "    return to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Auto_Feature_Engineering(df):\n",
    "    drop1 = Feature_Importance_IG(df)\n",
    "    dfh1 = df.drop(columns = drop1)\n",
    "    \n",
    "    drop2 = Feature_Redundancy_Pearson(dfh1)\n",
    "    dfh2 = dfh1.drop(columns = drop2)\n",
    "    \n",
    "    return dfh2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flow_ID</th>\n",
       "      <th>Src_IP</th>\n",
       "      <th>Src_Port</th>\n",
       "      <th>Dst_IP</th>\n",
       "      <th>Dst_Port</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Flow_Duration</th>\n",
       "      <th>TotLen_Bwd_Pkts</th>\n",
       "      <th>Flow_Byts/s</th>\n",
       "      <th>Flow_Pkts/s</th>\n",
       "      <th>Flow_IAT_Std</th>\n",
       "      <th>Fwd_IAT_Tot</th>\n",
       "      <th>Bwd_IAT_Std</th>\n",
       "      <th>Bwd_Pkts/s</th>\n",
       "      <th>Pkt_Len_Std</th>\n",
       "      <th>Init_Bwd_Win_Byts</th>\n",
       "      <th>Idle_Mean</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.198248</td>\n",
       "      <td>0.446464</td>\n",
       "      <td>0.821049</td>\n",
       "      <td>0.413136</td>\n",
       "      <td>0.148302</td>\n",
       "      <td>0.874767</td>\n",
       "      <td>0.001241</td>\n",
       "      <td>0.123680</td>\n",
       "      <td>0.220603</td>\n",
       "      <td>0.006440</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016109</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028534</td>\n",
       "      <td>0.002506</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000765</td>\n",
       "      <td>0.446413</td>\n",
       "      <td>0.931680</td>\n",
       "      <td>0.222458</td>\n",
       "      <td>0.127585</td>\n",
       "      <td>0.848929</td>\n",
       "      <td>0.466032</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.336694</td>\n",
       "      <td>0.458352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.829215</td>\n",
       "      <td>0.001938</td>\n",
       "      <td>0.311194</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.198045</td>\n",
       "      <td>0.446413</td>\n",
       "      <td>0.139234</td>\n",
       "      <td>0.419492</td>\n",
       "      <td>0.867104</td>\n",
       "      <td>0.525838</td>\n",
       "      <td>0.000726</td>\n",
       "      <td>0.061840</td>\n",
       "      <td>0.374723</td>\n",
       "      <td>0.010947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013679</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028534</td>\n",
       "      <td>0.001475</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.197967</td>\n",
       "      <td>0.446464</td>\n",
       "      <td>0.813747</td>\n",
       "      <td>0.413136</td>\n",
       "      <td>0.148302</td>\n",
       "      <td>0.456471</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008042</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000843</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006691</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.498657</td>\n",
       "      <td>0.001506</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.197967</td>\n",
       "      <td>0.446464</td>\n",
       "      <td>0.813747</td>\n",
       "      <td>0.413136</td>\n",
       "      <td>0.148302</td>\n",
       "      <td>0.442970</td>\n",
       "      <td>0.000827</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009627</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012028</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.494736</td>\n",
       "      <td>0.001678</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6247</th>\n",
       "      <td>0.197483</td>\n",
       "      <td>0.446464</td>\n",
       "      <td>0.156291</td>\n",
       "      <td>0.413136</td>\n",
       "      <td>0.148302</td>\n",
       "      <td>0.245577</td>\n",
       "      <td>0.000696</td>\n",
       "      <td>0.123680</td>\n",
       "      <td>0.390782</td>\n",
       "      <td>0.011417</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.028552</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028534</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6248</th>\n",
       "      <td>0.213862</td>\n",
       "      <td>0.446413</td>\n",
       "      <td>0.928608</td>\n",
       "      <td>0.483051</td>\n",
       "      <td>0.146312</td>\n",
       "      <td>0.759311</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>0.001426</td>\n",
       "      <td>0.039416</td>\n",
       "      <td>0.049988</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.024980</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6249</th>\n",
       "      <td>0.119352</td>\n",
       "      <td>0.269300</td>\n",
       "      <td>0.131007</td>\n",
       "      <td>0.413136</td>\n",
       "      <td>0.009109</td>\n",
       "      <td>0.860102</td>\n",
       "      <td>0.012935</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001539</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222794</td>\n",
       "      <td>0.025932</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6250</th>\n",
       "      <td>0.978750</td>\n",
       "      <td>0.446516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.483051</td>\n",
       "      <td>0.164217</td>\n",
       "      <td>0.742551</td>\n",
       "      <td>0.002230</td>\n",
       "      <td>0.001426</td>\n",
       "      <td>0.005682</td>\n",
       "      <td>0.007195</td>\n",
       "      <td>0.001788</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6251</th>\n",
       "      <td>0.736112</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.006838</td>\n",
       "      <td>0.423729</td>\n",
       "      <td>0.710894</td>\n",
       "      <td>0.767458</td>\n",
       "      <td>0.001846</td>\n",
       "      <td>0.064201</td>\n",
       "      <td>0.154344</td>\n",
       "      <td>0.004336</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005415</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003860</td>\n",
       "      <td>0.003719</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6252 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Flow_ID    Src_IP  Src_Port    Dst_IP  Dst_Port  Timestamp  \\\n",
       "0     0.198248  0.446464  0.821049  0.413136  0.148302   0.874767   \n",
       "1     0.000765  0.446413  0.931680  0.222458  0.127585   0.848929   \n",
       "2     0.198045  0.446413  0.139234  0.419492  0.867104   0.525838   \n",
       "3     0.197967  0.446464  0.813747  0.413136  0.148302   0.456471   \n",
       "4     0.197967  0.446464  0.813747  0.413136  0.148302   0.442970   \n",
       "...        ...       ...       ...       ...       ...        ...   \n",
       "6247  0.197483  0.446464  0.156291  0.413136  0.148302   0.245577   \n",
       "6248  0.213862  0.446413  0.928608  0.483051  0.146312   0.759311   \n",
       "6249  0.119352  0.269300  0.131007  0.413136  0.009109   0.860102   \n",
       "6250  0.978750  0.446516  1.000000  0.483051  0.164217   0.742551   \n",
       "6251  0.736112  0.000121  0.006838  0.423729  0.710894   0.767458   \n",
       "\n",
       "      Flow_Duration  TotLen_Bwd_Pkts  Flow_Byts/s  Flow_Pkts/s  Flow_IAT_Std  \\\n",
       "0          0.001241         0.123680     0.220603     0.006440      0.000000   \n",
       "1          0.466032         0.000000     0.000539     0.000023      0.336694   \n",
       "2          0.000726         0.061840     0.374723     0.010947      0.000000   \n",
       "3          0.001493         0.000000     0.000000     0.008042      0.000011   \n",
       "4          0.000827         0.000000     0.000000     0.009627      0.000000   \n",
       "...             ...              ...          ...          ...           ...   \n",
       "6247       0.000696         0.123680     0.390782     0.011417      0.000000   \n",
       "6248       0.000394         0.001426     0.039416     0.049988      0.000116   \n",
       "6249       0.012935         0.000000     0.000000     0.000611      0.000000   \n",
       "6250       0.002230         0.001426     0.005682     0.007195      0.001788   \n",
       "6251       0.001846         0.064201     0.154344     0.004336      0.000000   \n",
       "\n",
       "      Fwd_IAT_Tot  Bwd_IAT_Std  Bwd_Pkts/s  Pkt_Len_Std  Init_Bwd_Win_Byts  \\\n",
       "0        0.000000          0.0    0.016109     0.000000           0.028534   \n",
       "1        0.458352          0.0    0.000023     0.829215           0.001938   \n",
       "2        0.000000          0.0    0.013679     0.000000           0.028534   \n",
       "3        0.000843          0.0    0.006691     0.000000           0.498657   \n",
       "4        0.000000          0.0    0.012028     0.000000           0.494736   \n",
       "...           ...          ...         ...          ...                ...   \n",
       "6247     0.000000          0.0    0.028552     0.000000           0.028534   \n",
       "6248     0.000216          0.0    0.024980     0.000000           0.000000   \n",
       "6249     0.000000          0.0    0.001539     0.000000           0.222794   \n",
       "6250     0.000182          0.0    0.004484     0.000000           0.000000   \n",
       "6251     0.000000          0.0    0.005415     0.000000           0.003860   \n",
       "\n",
       "      Idle_Mean  Label  \n",
       "0      0.002506      1  \n",
       "1      0.311194      1  \n",
       "2      0.001475      1  \n",
       "3      0.001506      1  \n",
       "4      0.001678      1  \n",
       "...         ...    ...  \n",
       "6247   0.001415      1  \n",
       "6248   0.000202      1  \n",
       "6249   0.025932      1  \n",
       "6250   0.001496      1  \n",
       "6251   0.003719      1  \n",
       "\n",
       "[6252 rows x 18 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfh2 = Auto_Feature_Engineering(df)\n",
    "dfh2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Train-test split\n",
    "Split the dataset into the training and the test set   \n",
    "\n",
    "Here we used the 0.1%/99.9% split, because for online learning, the initial available data would only be a small percentage, and the incoming data streams will be continously learned by the data analytics models. It can be changed based on specific tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfh2.drop(['Label'],axis=1)\n",
    "y = dfh2['Label']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, train_size = 0.001, test_size = 0.999, shuffle=False,random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Model Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Four state-of-the-art online learners: \n",
    "* Hoeffding Tree (HT)\n",
    "* Leveraging Bagging (LB)\n",
    "* Adaptive Random Forest (ARF) \n",
    "* Streaming Random Patches (SRP)\n",
    "\n",
    "\n",
    "An offline learner for comparison:\n",
    "* LightGBM\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Static Model learning: LightGBM (for Comparison)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       361\n",
      "           1       0.94      1.00      0.97      5885\n",
      "\n",
      "    accuracy                           0.94      6246\n",
      "   macro avg       0.47      0.50      0.49      6246\n",
      "weighted avg       0.89      0.94      0.91      6246\n",
      "\n",
      "Accuracy: 94.22%\n",
      "Precision: 94.22%\n",
      "Recall: 100.0%\n",
      "F1-score: 97.024%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 185 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# LightGBM algorithm\n",
    "import lightgbm as lgb\n",
    "classifier = lgb.LGBMClassifier()\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))\n",
    "print(\"Accuracy: \"+str(round(accuracy_score(y_test, y_pred),5)*100)+\"%\")\n",
    "print(\"Precision: \"+str(round(precision_score(y_test, y_pred),5)*100)+\"%\")\n",
    "print(\"Recall: \"+str(round(recall_score(y_test, y_pred),5)*100)+\"%\")\n",
    "print(\"F1-score: \"+str(round(f1_score(y_test, y_pred),5)*100)+\"%\")\n",
    "\n",
    "# Plot the confusion matrix\n",
    "cm=confusion_matrix(y_test,y_pred)\n",
    "f,ax=plt.subplots(figsize=(5,5))\n",
    "sns.heatmap(cm,annot=True,linewidth=0.5,linecolor=\"red\",fmt=\".0f\",ax=ax)\n",
    "plt.xlabel(\"y_pred\")\n",
    "plt.ylabel(\"y_true\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy of the static LightGBM model is 94.22%, which can be improved by using online learning methods that can adapt to concept drifts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Online Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the online learning metrics and algorithms from the River library\n",
    "import river\n",
    "from river import metrics\n",
    "from river import stream\n",
    "from river import tree,neighbors,naive_bayes,ensemble,linear_model\n",
    "from river.drift import DDM, EDDM, ADWIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a generic adaptive learning function\n",
    "# The argument \"model\" means an online adaptive learning algorithm\n",
    "def adaptive_learning(model, X_train, y_train, X_test, y_test):\n",
    "    metric = metrics.Accuracy() # Use accuracy as the metric\n",
    "    i = 0 # count the number of evaluated data points\n",
    "    t = [] # record the number of evaluated data points\n",
    "    m = [] # record the real-time accuracy\n",
    "    yt = [] # record all the true labels of the test set\n",
    "    yp = [] # record all the predicted labels of the test set\n",
    "\n",
    "    # Learn the training set\n",
    "    for xi1, yi1 in stream.iter_pandas(X_train, y_train):\n",
    "        model.learn_one(xi1,yi1) \n",
    "\n",
    "    # Predict the test set\n",
    "    for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "        y_pred= model.predict_one(xi)  # Predict the test sample\n",
    "        model.learn_one(xi,yi) # Learn the test sample\n",
    "        metric = metric.update(yi, y_pred) # Update the real-time accuracy\n",
    "        t.append(i)\n",
    "        m.append(metric.get()*100)\n",
    "        yt.append(yi)\n",
    "        yp.append(y_pred)\n",
    "        i = i+1\n",
    "    print(\"Accuracy: \"+str(round(accuracy_score(yt,yp),5)*100)+\"%\")\n",
    "    print(\"Precision: \"+str(round(precision_score(yt,yp),5)*100)+\"%\")\n",
    "    print(\"Recall: \"+str(round(recall_score(yt,yp),5)*100)+\"%\")\n",
    "    print(\"F1-score: \"+str(round(f1_score(yt,yp),5)*100)+\"%\")\n",
    "    return t, m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a figure function that shows the real-time accuracy changes\n",
    "def acc_fig(t, m, name):\n",
    "    plt.rcParams.update({'font.size': 15})\n",
    "    plt.figure(1,figsize=(10,6)) \n",
    "    sns.set_style(\"darkgrid\")\n",
    "    plt.clf() \n",
    "    plt.plot(t,m,'-b',label='Avg Accuracy: %.2f%%'%(m[-1]))\n",
    "\n",
    "    plt.legend(loc='best')\n",
    "    plt.title(name+' on IoTID20 dataset', fontsize=15)\n",
    "    plt.xlabel('Number of samples')\n",
    "    plt.ylabel('Accuracy (%)')\n",
    "\n",
    "    plt.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 95.757%\n",
      "Precision: 95.89999999999999%\n",
      "Recall: 99.762%\n",
      "F1-score: 97.79299999999999%\n",
      "Wall time: 1.41 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Use the Hoeffding Tree (HT) model \n",
    "name1 = \"HT model\"\n",
    "model1 = tree.HoeffdingTreeClassifier() # Define the model\n",
    "t, m1 = adaptive_learning(model1, X_train, y_train, X_test, y_test) # Learn the model on the dataset\n",
    "acc_fig(t, m1, name1) # Draw the figure of how the real-time accuracy changes with the number of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 97.82300000000001%\n",
      "Precision: 98.63%\n",
      "Recall: 99.065%\n",
      "F1-score: 98.847%\n",
      "Wall time: 18.9 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Use the Leveraging Bagging (LB) model \n",
    "name2 = \"LB model\"\n",
    "model2 = ensemble.LeveragingBaggingClassifier(model=tree.HoeffdingTreeClassifier(),n_models=3) # Define the model\n",
    "t, m2 = adaptive_learning(model2, X_train, y_train, X_test, y_test) # Learn the model on the dataset\n",
    "acc_fig(t, m2, name2) # Draw the figure of how the real-time accuracy changes with the number of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 97.342%\n",
      "Precision: 97.587%\n",
      "Recall: 99.643%\n",
      "F1-score: 98.604%\n",
      "Wall time: 4.03 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Use the Adaptive Random Forest (ARF) model\n",
    "name3 = \"ARF model\"\n",
    "model3 = ensemble.AdaptiveRandomForestClassifier(n_models = 3) # Define the model\n",
    "t, m3 = adaptive_learning(model3, X_train, y_train, X_test, y_test) # Learn the model on the dataset\n",
    "acc_fig(t, m3, name3) # Draw the figure of how the real-time accuracy changes with the number of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 98.735%\n",
      "Precision: 99.203%\n",
      "Recall: 99.456%\n",
      "F1-score: 99.33%\n",
      "Wall time: 12.9 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Use the Streaming Random Patches (SRP) model with ADWIN drift detector\n",
    "name4 = \"SRP model\"\n",
    "model4 = ensemble.SRPClassifier(n_models = 3) # Define the model\n",
    "t, m4 = adaptive_learning(model4, X_train, y_train, X_test, y_test) # Learn the model on the dataset\n",
    "acc_fig(t, m4, name4) # Draw the figure of how the real-time accuracy changes with the number of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a comprehensive figure to compare the performance of all models\n",
    "plt.rcParams.update({'font.size': 30})\n",
    "plt.figure(1,figsize=(24,15)) \n",
    "sns.set_style(\"darkgrid\")\n",
    "plt.clf() \n",
    "\n",
    "# Plot the accuracy change of each learner\n",
    "plt.plot(t,m1,'-b',label=name1+', Avg Accuracy: %.2f%%'%(m1[-1]))\n",
    "plt.plot(t,m2,'-g',label=name2+', Avg Accuracy: %.2f%%'%(m2[-1]))\n",
    "plt.plot(t,m3,'orange',label=name3+', Avg Accuracy: %.2f%%'%(m3[-1]))\n",
    "plt.plot(t,m4,'black',label=name4+', Avg Accuracy: %.2f%%'%(m4[-1]))\n",
    "\n",
    "# Draw the drift points/time\n",
    "dr = [0,270,600]\n",
    "for i in range(len(dr)):\n",
    "    if i!=0:\n",
    "        plt.text(dr[i]-200, 100.2, 'Drift '+str(i), c = \"red\", fontsize = 25)\n",
    "        plt.vlines(dr[i], 0, 100, colors = \"red\", linewidth=4, linestyles = \"dashed\")\n",
    "        \n",
    "plt.legend(loc='lower right')\n",
    "plt.ylim(85, 100.8)\n",
    "plt.title('IoTID20', fontsize=40)\n",
    "plt.xlabel('Number of samples')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "\n",
    "plt.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automated Online Learning Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the best-performing model among four state-of-the-art online learning models (HT, LB, ARF, and SRP) by evaluating their learning performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a generic adaptive learning function\n",
    "# The argument \"model\" means an online adaptive learning algorithm\n",
    "def adaptive_learning_model_selection(model_list, X_train, y_train, X_test, y_test):\n",
    "    metric = metrics.Accuracy() # Use accuracy as the metric\n",
    "    i = 0 # count the number of evaluated data points\n",
    "    t = [] # record the number of evaluated data points\n",
    "    m = [] # record the real-time accuracy\n",
    "    yt = [] # record all the true labels of the test set\n",
    "    yp = [] # record all the predicted labels of the test set\n",
    "    ypb = [] # record all the predicted labels of the test set for the best performing model\n",
    "    acc_b = 0 # The best accuracy score\n",
    "    mb = [] # record the real-time accuracy of the best performing model\n",
    "    \n",
    "    for model in model_list:\n",
    "        # Learn the training set\n",
    "        for xi1, yi1 in stream.iter_pandas(X_train, y_train):\n",
    "            model.learn_one(xi1,yi1) \n",
    "\n",
    "        # Predict the test set\n",
    "        for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "            y_pred= model.predict_one(xi)  # Predict the test sample\n",
    "            model.learn_one(xi,yi) # Learn the test sample\n",
    "            metric = metric.update(yi, y_pred) # Update the real-time accuracy\n",
    "            t.append(i)\n",
    "            m.append(metric.get()*100)\n",
    "            yt.append(yi)\n",
    "            yp.append(y_pred)\n",
    "            i = i+1\n",
    "#         print(accuracy_score(yt,yp))\n",
    "        if accuracy_score(yt,yp)>acc_b:\n",
    "            acc_b = accuracy_score(yt,yp)\n",
    "            mb = m\n",
    "            ypb = yp\n",
    "            model_b = model\n",
    "            \n",
    "    print(model_b)    \n",
    "    print(\"Accuracy: \"+str(round(accuracy_score(yt,ypb),5)*100)+\"%\")\n",
    "    print(\"Precision: \"+str(round(precision_score(yt,ypb),5)*100)+\"%\")\n",
    "    print(\"Recall: \"+str(round(recall_score(yt,ypb),5)*100)+\"%\")\n",
    "    print(\"F1-score: \"+str(round(f1_score(yt,ypb),5)*100)+\"%\")\n",
    "    return t, mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SRPClassifier(HoeffdingTreeClassifier)\n",
      "Accuracy: 97.787%\n",
      "Precision: 98.092%\n",
      "Recall: 99.588%\n",
      "F1-score: 98.834%\n"
     ]
    }
   ],
   "source": [
    "model1 = tree.HoeffdingTreeClassifier() \n",
    "model2 = ensemble.LeveragingBaggingClassifier(model=tree.HoeffdingTreeClassifier(),n_models=3)\n",
    "model3 = ensemble.AdaptiveRandomForestClassifier(n_models = 3)\n",
    "model4 = ensemble.SRPClassifier(n_models = 3)\n",
    "model_list = [model1, model2, model3, model4]\n",
    "t, mb = adaptive_learning_model_selection(model_list, X_train, y_train, X_test, y_test) # Learn the model on the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SRP with the highest accuracy is selected as the best/most suitable online learner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 4. Hyperparameter Optimization\n",
    "Optimize the best performing online learning model (SRP) by tuning its hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 98.527%                                                                                                      \n",
      "Precision: 99.285%                                                                                                     \n",
      "Recall: 99.15%                                                                                                         \n",
      "F1-score: 99.21799999999999%                                                                                           \n",
      "Accuracy: 98.735%                                                                                                      \n",
      "Precision: 99.187%                                                                                                     \n",
      "Recall: 99.473%                                                                                                        \n",
      "F1-score: 99.33%                                                                                                       \n",
      "Accuracy: 99.119%                                                                                                      \n",
      "Precision: 99.373%                                                                                                     \n",
      "Recall: 99.694%                                                                                                        \n",
      "F1-score: 99.533%                                                                                                      \n",
      "Accuracy: 98.87899999999999%                                                                                           \n",
      "Precision: 99.188%                                                                                                     \n",
      "Recall: 99.626%                                                                                                        \n",
      "F1-score: 99.407%                                                                                                      \n",
      "Accuracy: 99.087%                                                                                                      \n",
      "Precision: 99.39%                                                                                                      \n",
      "Recall: 99.643%                                                                                                        \n",
      "F1-score: 99.516%                                                                                                      \n",
      "Accuracy: 99.007%                                                                                                      \n",
      "Precision: 99.356%                                                                                                     \n",
      "Recall: 99.592%                                                                                                        \n",
      "F1-score: 99.47399999999999%                                                                                           \n",
      "Accuracy: 98.991%                                                                                                      \n",
      "Precision: 99.289%                                                                                                     \n",
      "Recall: 99.643%                                                                                                        \n",
      "F1-score: 99.466%                                                                                                      \n",
      "Accuracy: 98.847%                                                                                                      \n",
      "Precision: 99.20400000000001%                                                                                          \n",
      "Recall: 99.575%                                                                                                        \n",
      "F1-score: 99.38900000000001%                                                                                           \n",
      "Accuracy: 98.847%                                                                                                      \n",
      "Precision: 99.188%                                                                                                     \n",
      "Recall: 99.592%                                                                                                        \n",
      "F1-score: 99.39%                                                                                                       \n",
      "Accuracy: 98.751%                                                                                                      \n",
      "Precision: 99.17%                                                                                                      \n",
      "Recall: 99.507%                                                                                                        \n",
      "F1-score: 99.33800000000001%                                                                                           \n",
      "100%|████████████████████████████████████████████████| 10/10 [03:42<00:00, 22.22s/trial, best loss: -99.11943643932116]\n",
      "SRP: Hyperopt estimated optimum {'n_models': 4.0, 'subspace_size': 0.46191927562659807}\n"
     ]
    }
   ],
   "source": [
    "from hyperopt import hp, fmin, tpe, STATUS_OK, Trials\n",
    "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
    "\n",
    "# Define the objective function\n",
    "def objective(params):\n",
    "    params = {\n",
    "        'n_models': int(params['n_models']), \n",
    "        'subspace_size': abs(float(params['subspace_size'])),\n",
    "    }\n",
    "    clf = ensemble.SRPClassifier( **params)\n",
    "    \n",
    "    t, m = adaptive_learning(clf, X_train, y_train, X_test, y_test)\n",
    "    \n",
    "    return {'loss':-m[-1], 'status': STATUS_OK }\n",
    "\n",
    "# Define the hyperparameter configuration space\n",
    "space = {\n",
    "    'n_models': hp.quniform('n_models', 2, 10, 1),\n",
    "    \"subspace_size\":hp.uniform('subspace_size', 0.3, 1),\n",
    "}\n",
    "\n",
    "# Detect the optimal hyperparameter values\n",
    "best = fmin(fn=objective,\n",
    "            space=space,\n",
    "            algo=tpe.suggest,\n",
    "            max_evals=10)\n",
    "print(\"SRP: Hyperopt estimated optimum {}\".format(best))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "After hyperparameter optimization, the accuracy of SRP has been improved from 97.787% to 99.119%"
   ]
  }
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