3-OASW_for_concept_drift_detection&adaptation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams \n",
    "This is the code for the paper entitled \"**A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams**\" accepted in IEEE Internet of Things Magazine.  \n",
    "Authors: Li Yang (lyang339@uwo.ca) and Abdallah Shami (Abdallah.Shami@uwo.ca)  \n",
    "Organization: The Optimized Computing and Communications (OC2) Lab, ECE Department, Western University\n",
    "\n",
    "**Notebook 3: Proposed Optimized Adaptive and Sliding Windowing (OASW) method for concept drift detection and adaptation**  \n",
    "Aims:  \n",
    "&nbsp; 1): Develop a drift detection and adaptation method to handle concept drift   \n",
    "&nbsp; 2): Obtain the optimized drift model using hyperparameter optimization techniques  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import lightgbm as lgb\n",
    "from sklearn.metrics import accuracy_score\n",
    "from river import metrics\n",
    "from river import stream\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the NSL-KDD dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"NSL_KDD_binary(train+test).csv\")\n",
    "df1 = pd.read_csv(\"NSL_KDD_binary_train.csv\")\n",
    "df2 = pd.read_csv(\"NSL_KDD_binary_test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data sampling\n",
    "Use the last 10% of training set and the entire test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_used = int(len(df1)*0.1) + len(df2)\n",
    "df0 = df.iloc[-df_used:]\n",
    "\n",
    "# df0=df0.sample(n=None, frac=0.1, replace=False, weights=None, random_state=None, axis=0)\n",
    "# df0=df0.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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      "text/plain": [
       "        duration  protocol_type  service  flag  src_bytes  dst_bytes  land  \\\n",
       "113376         0              1       54     9       1132        366     0   \n",
       "113377         0              1       24     9        256       3123     0   \n",
       "113378         1              1       54     9       2215        333     0   \n",
       "113379         0              1        5     5          0          0     0   \n",
       "113380         0              1       56     5          0          0     0   \n",
       "...          ...            ...      ...   ...        ...        ...   ...   \n",
       "148512         0              1       49     9        794        333     0   \n",
       "148513         0              1       22     9        317        938     0   \n",
       "148514         0              1       22     9      54540       8314     0   \n",
       "148515         0              2       11     9         42         42     0   \n",
       "148516         0              1       52     1          0          0     0   \n",
       "\n",
       "        wrong_fragment  urgent  hot  ...  dst_host_srv_count  \\\n",
       "113376               0       0    0  ...                 148   \n",
       "113377               0       0    0  ...                 255   \n",
       "113378               0       0    0  ...                  99   \n",
       "113379               0       0    0  ...                   1   \n",
       "113380               0       0    0  ...                   1   \n",
       "...                ...     ...  ...  ...                 ...   \n",
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       "148514               0       0    2  ...                 255   \n",
       "148515               0       0    0  ...                 252   \n",
       "148516               0       0    0  ...                  21   \n",
       "\n",
       "        dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "113376                    0.88                    0.08   \n",
       "113377                    1.00                    0.00   \n",
       "113378                    0.39                    0.08   \n",
       "113379                    0.00                    0.07   \n",
       "113380                    0.00                    0.08   \n",
       "...                        ...                     ...   \n",
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       "\n",
       "        dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "113376                         0.03                         0.01   \n",
       "113377                         0.01                         0.02   \n",
       "113378                         0.00                         0.00   \n",
       "113379                         0.00                         0.00   \n",
       "113380                         0.00                         0.00   \n",
       "...                             ...                          ...   \n",
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       "148515                         0.00                         0.00   \n",
       "148516                         0.00                         0.00   \n",
       "\n",
       "        dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "113376                  0.00                       0.0                  0.00   \n",
       "113377                  0.00                       0.0                  0.00   \n",
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       "113379                  1.00                       1.0                  0.00   \n",
       "113380                  1.00                       1.0                  0.00   \n",
       "...                      ...                       ...                   ...   \n",
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       "148515                  0.00                       0.0                  0.00   \n",
       "148516                  0.00                       0.0                  0.44   \n",
       "\n",
       "        dst_host_srv_rerror_rate  label  \n",
       "113376                      0.00      0  \n",
       "113377                      0.00      0  \n",
       "113378                      0.00      0  \n",
       "113379                      0.00      1  \n",
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       "...                          ...    ...  \n",
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       "148515                      0.00      0  \n",
       "148516                      1.00      1  \n",
       "\n",
       "[35141 rows x 42 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train-test split\n",
    "10% training set, and 90% test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = df0.drop(['label'],axis=1)\n",
    "y = df0['label']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, train_size = 0.1, test_size = 0.9, shuffle=False, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Offline learning: LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory (Before): 264.24609375 MB\n",
      "Accuracy: 0.8420969424858507\n",
      "Memory (After) : 269.6640625 MB\n",
      "Memory Consumption : 5.41796875 MB\n",
      "Wall time: 374 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import memory_profiler as mem_profile\n",
    "m1 = mem_profile.memory_usage()\n",
    "print('Memory (Before): ' + str(m1[0]) + ' MB' )\n",
    "\n",
    "classifier = lgb.LGBMClassifier()\n",
    "# classifier = joblib.load('Optimized_lightGBM.pkl')\n",
    "classifier.fit(X_train,y_train)\n",
    "predictions = classifier.predict(X_test)\n",
    "print(\"Accuracy: \"+str(accuracy_score(y_test,predictions)))\n",
    "\n",
    "m2 = mem_profile.memory_usage()\n",
    "print('Memory (After) : ' + str(m2[0]) + ' MB')\n",
    "print('Memory Consumption : ' + str(m2[0]-m1[0]) + ' MB')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OASW method Development"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the OASW method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import time\n",
    "from hyperopt import hp, fmin, tpe, STATUS_OK, Trials\n",
    "\n",
    "# a: warining threshold, b: drift threshold, win1: sliding window size, win2: adaptive window max size\n",
    "def oasw_bo(params):\n",
    "\n",
    "    a=abs(float(params['a']))\n",
    "    b=abs(float(params['b']))\n",
    "    win1=int(params['win1'])\n",
    "    win2=int(params['win2'])\n",
    " \n",
    "    i=0\n",
    "    yt = []\n",
    "    yp = []\n",
    "    x_new=[]\n",
    "    y_new=[]\n",
    "    dr=[0]\n",
    "    d=0\n",
    "    f=0\n",
    "    tt=0\n",
    "    th=0\n",
    "    xt=[]\n",
    "    \n",
    "    metric = metrics.Accuracy()\n",
    "    \n",
    "    classifier = lgb.LGBMClassifier() # Train the initial LightGBM model\n",
    "    classifier.fit(X_train,y_train)\n",
    "    \n",
    "    for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "\n",
    "        xi2=np.array(list(xi.values()))\n",
    "        y_pred = classifier.predict(xi2.reshape(1, -1))      # make a prediction\n",
    "        metric = metric.update(yi, y_pred)  # update the metric\n",
    "\n",
    "        # Store the y_test, y_pred, and x_test\n",
    "        yt.append(yi)\n",
    "        yp.append(y_pred)\n",
    "        xt.append(xi2)\n",
    "\n",
    "        # Monitor the accuracy changes in the sliding window\n",
    "        if i>2*win1:\n",
    "            acc1=accuracy_score(yt[i-win1:],yp[i-win1:]) # Current window accuracy\n",
    "            acc2=accuracy_score(yt[i-2*win1:i-win1],yp[i-2*win1:i-win1]) # Last window accuracy\n",
    "            if (d==0)&(acc1<a*acc2): # If the window accuracy drops to the warning level\n",
    "                x_new.append(xi2)\n",
    "                y_new.append(yi)\n",
    "                d=1\n",
    "            if d==1: # In the warning level\n",
    "                tt=len(y_new)\n",
    "                if acc1<b*acc2: # If the window accuracy drops to the drift level\n",
    "                    dr.append(i)# Record the drift start point\n",
    "                    f=i\n",
    "                    if tt<win1: # if enough new concept samples are collected\n",
    "                        classifier.fit(xt[i-win1:],yt[i-win1:]) \n",
    "                    else:\n",
    "                        classifier.fit(x_new,y_new)\n",
    "                    d=2\n",
    "                elif (acc1>a*acc2)|(tt==win2): # If the window accuracy increases back to the normal level (false alarm)\n",
    "                    x_new=[]\n",
    "                    y_new=[]\n",
    "                    d=0\n",
    "                else:\n",
    "                    x_new.append(xi2)\n",
    "                    y_new.append(yi)\n",
    "            if d==2: # In the drift level\n",
    "                tt=len(y_new)\n",
    "                acc3=accuracy_score(yt[f:f+win1],yp[f:f+win1])\n",
    "                x_new.append(xi2)\n",
    "                y_new.append(yi)\n",
    "                if tt>=win1:\n",
    "                    if (acc1<a*acc3): # When new concept accuracy drops to the warning level\n",
    "                        if th==0:\n",
    "                            classifier.fit(x_new,y_new) # Retrain the classifier on all the newly collected samples to obtain a robust classifier\n",
    "                            th=1\n",
    "                    if (th==1)&(tt==win2): # When sufficient new concept samples are collected\n",
    "                        classifier.fit(x_new,y_new) #obtain a robust classifier\n",
    "                        x_new=[]\n",
    "                        y_new=[]\n",
    "                        d=0 # Go back to the normal state for next potential drift detection\n",
    "                        th=0\n",
    "\n",
    "        i=i+1\n",
    "    score=metric.get()\n",
    "    print(str(a)+\" \"+str(b)+\" \"+str(win1)+\" \"+str(win2)+\" \"+str(score)) # Output the hyperparameter values and corresponding accuracy\n",
    "    return {'loss': -score, 'status': STATUS_OK }\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter optimization by BO-TPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9570000000000001 0.925 600 1600 0.9487828011381622\n",
      "0.977 0.96 700 4300 0.9487828011381622\n",
      "0.965 0.901 300 4300 0.9487828011381622\n",
      "0.981 0.905 600 4600 0.9487828011381622\n",
      "0.985 0.9560000000000001 950 1400 0.9487828011381622\n",
      "0.989 0.91 500 2100 0.9487828011381622\n",
      "0.969 0.965 900 3800 0.9487828011381622\n",
      "0.9530000000000001 0.972 1000 4300 0.9487828011381622\n",
      "0.963 0.912 450 3800 0.9487828011381622\n",
      "0.9580000000000001 0.9560000000000001 650 4100 0.9487828011381622\n",
      "100%|███████████████████████████████████████████████| 10/10 [00:51<00:00,  5.14s/trial, best loss: -0.9487828011381622]\n",
      "Hyperopt estimated optimum {'a': 0.9570000000000001, 'b': 0.925, 'win1': 600.0, 'win2': 1600.0}\n",
      "51.43736743927002\n"
     ]
    }
   ],
   "source": [
    "from hyperopt import hp, fmin, tpe, STATUS_OK, Trials\n",
    "space = {\n",
    "    'a': hp.quniform('a', 0.95, 0.99, 0.001),\n",
    "    'b': hp.quniform('b', 0.90, 0.98, 0.001),\n",
    "    'win1': hp.quniform('win1', 200, 1000, 50),\n",
    "    'win2': hp.quniform('win2', 1000, 5000, 100),\n",
    "}\n",
    "\n",
    "t1=time.time()\n",
    "best = fmin(fn=oasw_bo,\n",
    "            space=space,\n",
    "            algo=tpe.suggest,\n",
    "            max_evals=10)\n",
    "print(\"Hyperopt estimated optimum {}\".format(best))\n",
    "t2=time.time()\n",
    "print(t2-t1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter optimization by PSO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def oasw(a=0.99, b=0.95, win1=200, win2=1000):\n",
    "    a = round(a, 3)\n",
    "    b = round(b, 3)\n",
    "    win1 = round(win1)\n",
    "    win2 = round(win2)\n",
    "\n",
    "    i=0\n",
    "    yt = []\n",
    "    yp = []\n",
    "    x_new=[]\n",
    "    y_new=[]\n",
    "    dr=[0]\n",
    "    d=0\n",
    "    f=0\n",
    "    tt=0\n",
    "    th=0\n",
    "    xt=[]\n",
    "    \n",
    "    metric = metrics.Accuracy()\n",
    "    \n",
    "    classifier = lgb.LGBMClassifier() # Train the initial LightGBM model\n",
    "    classifier.fit(X_train,y_train)\n",
    "    \n",
    "    for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "\n",
    "        xi2=np.array(list(xi.values()))\n",
    "        y_pred = classifier.predict(xi2.reshape(1, -1))      # make a prediction\n",
    "        metric = metric.update(yi, y_pred)  # update the metric\n",
    "\n",
    "        # Store the y_test, y_pred, and x_test\n",
    "        yt.append(yi)\n",
    "        yp.append(y_pred)\n",
    "        xt.append(xi2)\n",
    "\n",
    "        # Monitor the accuracy changes in the sliding window\n",
    "        if i>2*win1:\n",
    "            acc1=accuracy_score(yt[i-win1:],yp[i-win1:]) # Current window accuracy\n",
    "            acc2=accuracy_score(yt[i-2*win1:i-win1],yp[i-2*win1:i-win1]) # Last window accuracy\n",
    "            if (d==0)&(acc1<a*acc2): # If the window accuracy drops to the warning level\n",
    "                x_new.append(xi2)\n",
    "                y_new.append(yi)\n",
    "                d=1\n",
    "            if d==1: # In the warning level\n",
    "                tt=len(y_new)\n",
    "                if acc1<b*acc2: # If the window accuracy drops to the drift level\n",
    "                    dr.append(i)# Record the drift start point\n",
    "                    f=i\n",
    "                    if tt<win1: # if enough new concept samples are collected\n",
    "                        classifier.fit(xt[i-win1:],yt[i-win1:]) \n",
    "                    else:\n",
    "                        classifier.fit(x_new,y_new)\n",
    "                    d=2\n",
    "                elif (acc1>a*acc2)|(tt==win2): # If the window accuracy increases back to the normal level (false alarm)\n",
    "                    x_new=[]\n",
    "                    y_new=[]\n",
    "                    d=0\n",
    "                else:\n",
    "                    x_new.append(xi2)\n",
    "                    y_new.append(yi)\n",
    "            if d==2: # In the drift level\n",
    "                tt=len(y_new)\n",
    "                acc3=accuracy_score(yt[f:f+win1],yp[f:f+win1])\n",
    "                x_new.append(xi2)\n",
    "                y_new.append(yi)\n",
    "                if tt>=win1:\n",
    "                    if (acc1<a*acc3): # When new concept accuracy drops to the warning level\n",
    "                        if th==0:\n",
    "                            classifier.fit(x_new,y_new) # Retrain the classifier on all the newly collected samples to obtain a robust classifier\n",
    "                            th=1\n",
    "                    if (th==1)&(tt==win2): # When sufficient new concept samples are collected\n",
    "                        classifier.fit(x_new,y_new) #obtain a robust classifier\n",
    "                        x_new=[]\n",
    "                        y_new=[]\n",
    "                        d=0 # Go back to the normal state for next potential drift detection\n",
    "                        th=0\n",
    "\n",
    "        i=i+1\n",
    "    score=metric.get()\n",
    "    print(str(a)+\" \"+str(b)+\" \"+str(win1)+\" \"+str(win2)+\" \"+str(score)) # Output the hyperparameter values and corresponding accuracy\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.963359375 0.94109375 711 2820 0.9405627568763846\n",
      "0.968359375 0.91109375 411 3320 0.9509958899778705\n",
      "0.988359375 0.95109375 811 1320 0.927600379386658\n",
      "0.978359375 0.93109375 611 4320 0.9528928232690504\n",
      "0.958359375 0.97109375 211 2320 0.9494151122352197\n",
      "0.955859375 0.90609375 761 1070 0.9310780904204892\n",
      "0.975859375 0.94609375 361 3070 0.9500474233322783\n",
      "0.985859375 0.92609375 561 2070 0.9478343344925717\n",
      "0.965859375 0.96609375 961 4070 0.900727157761619\n",
      "0.960859375 0.93609375 261 1570 0.9503635788808115\n",
      "{'a': 0.978359375, 'b': 0.93109375, 'win1': 610.9375, 'win2': 4320.3125}\n",
      "Accuracy:0.9528928232690504\n"
     ]
    }
   ],
   "source": [
    "import optunity\n",
    "import optunity.metrics\n",
    "\n",
    "search = {\n",
    "    'a': [0.95,0.99],\n",
    "    'b': [0.90,0.98],\n",
    "    'win1': [200,1000],\n",
    "    'win2': [1000,5000],\n",
    "         }\n",
    "\n",
    "optimal_configuration, info, _ = optunity.maximize(oasw,\n",
    "                                                  solver_name='particle swarm',\n",
    "                                                  num_evals=10,\n",
    "                                                   **search\n",
    "                                                  )\n",
    "print(optimal_configuration)\n",
    "print(\"Accuracy:\"+ str(info.optimum))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the optimized OASW model with optimal hyperparameters\n",
    "The accuracy change figure is also plotted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Define OASW with a figure\n",
    "def oasw_plot(a=0.99, b=0.95, win1=200, win2=1000):\n",
    "    a = round(a, 3)\n",
    "    b = round(b, 3)\n",
    "    win1 = round(win1)\n",
    "    win2 = round(win2)\n",
    "    \n",
    "    metric = metrics.Accuracy()\n",
    "    metric2 = metrics.Accuracy()\n",
    "\n",
    "    i = 0\n",
    "    t = []\n",
    "    yt = []\n",
    "    yp = []\n",
    "    m = []\n",
    "    m2 = []\n",
    "    x_new=[]\n",
    "    y_new=[]\n",
    "    dr=[0]\n",
    "    d=0\n",
    "    f=0\n",
    "    tt=0\n",
    "    th=0\n",
    "    xt=[]\n",
    "    \n",
    "    classifier = lgb.LGBMClassifier() # Train the initial LightGBM model\n",
    "    classifier.fit(X_train,y_train)\n",
    "    \n",
    "    classifier2 = lgb.LGBMClassifier() # Train an offline LightGBM model as a comparison model\n",
    "    classifier2.fit(X_train,y_train)\n",
    "\n",
    "    for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "\n",
    "        xi2=np.array(list(xi.values()))\n",
    "        y_pred = classifier.predict(xi2.reshape(1, -1))      # make a prediction\n",
    "        metric = metric.update(yi, y_pred)  # update the metric\n",
    "\n",
    "        y_pred2= classifier2.predict(xi2.reshape(1, -1)) \n",
    "        metric2 = metric2.update(yi, y_pred2)\n",
    "        \n",
    "        # Store the y_test, y_pred, x_test, and real-time accuracy\n",
    "        t.append(i)\n",
    "        m.append(metric.get()*100)\n",
    "        yt.append(yi)\n",
    "        yp.append(y_pred)\n",
    "        m2.append(metric2.get()*100)\n",
    "        xt.append(xi2)\n",
    "        \n",
    "        # Monitor the accuracy changes in the sliding window\n",
    "        if i>2*win1:\n",
    "            acc1=accuracy_score(yt[i-win1:],yp[i-win1:]) # Current window accuracy\n",
    "            acc2=accuracy_score(yt[i-2*win1:i-win1],yp[i-2*win1:i-win1]) # Last window accuracy\n",
    "            if (d==0)&(acc1<a*acc2): # If the window accuracy drops to the warning level\n",
    "                x_new.append(xi2)\n",
    "                y_new.append(yi)\n",
    "                d=1\n",
    "            if d==1: # In the warning level\n",
    "                tt=len(y_new)\n",
    "                if acc1<b*acc2: # If the window accuracy drops to the drift level\n",
    "                    dr.append(i)# Record the drift start point\n",
    "                    f=i\n",
    "                    if tt<win1: # if enough new concept samples are collected\n",
    "                        classifier.fit(xt[i-win1:],yt[i-win1:]) \n",
    "                    else:\n",
    "                        classifier.fit(x_new,y_new)\n",
    "                    d=2\n",
    "                elif (acc1>a*acc2)|(tt==win2): # If the window accuracy increases back to the normal level (false alarm)\n",
    "                    x_new=[]\n",
    "                    y_new=[]\n",
    "                    d=0\n",
    "                else:\n",
    "                    x_new.append(xi2)\n",
    "                    y_new.append(yi)\n",
    "            \n",
    "            if d==2: # In the drift level\n",
    "                tt=len(y_new)\n",
    "                acc3=accuracy_score(yt[f:f+win1],yp[f:f+win1])\n",
    "                x_new.append(xi2)\n",
    "                y_new.append(yi)\n",
    "                if tt>=win1:\n",
    "                    if (acc1<a*acc3): # When new concept accuracy drops to the warning level\n",
    "                        if th==0:\n",
    "                            classifier.fit(x_new,y_new) # Retrain the classifier on all the newly collected samples to obtain a robust classifier\n",
    "                            th=1\n",
    "                    if (th==1)&(tt==win2): # When sufficient new concept samples are collected\n",
    "                        classifier.fit(x_new,y_new) #obtain a robust classifier\n",
    "                        x_new=[]\n",
    "                        y_new=[]\n",
    "                        d=0 # Go back to the normal state for next potential drift detection\n",
    "                        th=0\n",
    "\n",
    "        i=i+1\n",
    "    \n",
    "    # Plot the accuracy change figure\n",
    "    plt.rcParams.update({'font.size': 35})\n",
    "    plt.ion() \n",
    "    plt.figure(1,figsize=(24,15)) \n",
    "    sns.set_style(\"darkgrid\")\n",
    "    plt.clf() \n",
    "    plt.plot(t,m,'-b',label='OASW+LightGBM, Avg Accuracy: %.2f%%'%(metric.get()*100))\n",
    "    plt.plot(t,m2,'red',label='Offline LightGBM, Avg Avg Accuracy: %.2f%%'%(metric2.get()*100))\n",
    "    \n",
    "    #Plot the drift points\n",
    "    for i in range(len(dr)):\n",
    "        if i!=0:\n",
    "            plt.scatter(dr[i],m[dr[i]],s=200,c='r')\n",
    "\n",
    "    plt.legend(loc='best')\n",
    "    plt.ylim(80, 101)\n",
    "    plt.title('NSL-KDD', fontsize=40)\n",
    "    plt.xlabel('Number of samples')\n",
    "    plt.ylabel('Accuracy(%)')\n",
    "\n",
    "    plt.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assign the optimal hyperparameters detected by PSO\n",
    "oasw_plot(a = 0.978, b = 0.931, win1 = 611, win2 = 4320)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "tf36",
   "language": "python",
   "name": "tf36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}