0002 Feature Selection/02.1 Feature importance voting and pre-assessment of features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import math\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# create binary datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def folder(f_name): #this function creates a folder.\n",
    "    try:\n",
    "        if not os.path.exists(f_name):\n",
    "            os.makedirs(f_name)\n",
    "    except OSError:\n",
    "        print (\"The folder could not be created!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def target_name(name):\n",
    "    df = pd.read_csv(name,usecols=[\"Label\"])\n",
    "    target_names=sorted(list(df[\"Label\"].unique()))\n",
    "    target_names = list(map(str, target_names))\n",
    "    #print(target_names)\n",
    "    return target_names\n",
    "train=\"./Aalto_BIG_train_IoTDevID.csv\"\n",
    "device_names=target_name(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder(\"devicebasedcsvs\")\n",
    "folder(\"tree\")\n",
    "folder(\"results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aria\n",
      "D-LinkCam\n",
      "D-LinkDayCam\n",
      "D-LinkDoorSensor\n",
      "D-LinkHomeHub\n",
      "D-LinkSensor\n",
      "D-LinkSiren\n",
      "D-LinkSwitch\n",
      "D-LinkWaterSensor\n",
      "EdimaxCam\n",
      "EdimaxPlug1101W\n",
      "EdimaxPlug2101W\n",
      "EdnetCam\n",
      "EdnetGateway\n",
      "HomeMaticPlug\n",
      "HueBridge\n",
      "HueSwitch\n",
      "IKettle2\n",
      "Lightify\n",
      "MAXGateway\n",
      "SmarterCoffee\n",
      "TP-LinkPlugHS100\n",
      "TP-LinkPlugHS110\n",
      "WeMoInsightSwitch\n",
      "WeMoLink\n",
      "WeMoSwitch\n",
      "Withings\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for device in device_names:\n",
    "    print(device)\n",
    "    df=pd.read_csv(train)\n",
    "    #print(df.groupby(\"Label\").size())\n",
    "    k= df['Label'].values==device\n",
    "    del df[\"MAC\"]\n",
    "    k=list(k*1)\n",
    "    size=k.count(1)\n",
    "    df['Label']=k\n",
    "    if size>1000:\n",
    "        size=1000\n",
    "    dev=df[df[\"Label\"]==1]\n",
    "    notdev=df[df[\"Label\"]==0]\n",
    "    dev=dev.sample(n=size, random_state=1)\n",
    "    notdev=notdev.sample(n=size*5, random_state=1)\n",
    "    df = pd.concat([dev, notdev])\n",
    "    #df=df.groupby('Label').apply(lambda x: x.sample(n=size)).reset_index(drop = True)\n",
    "    df.to_csv(\"./devicebasedcsvs/\"+device+\".csv\",  index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# create decision tree diagrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "import graphviz\n",
    "from graphviz import render\n",
    "\n",
    "def ciz(name,model,feature_names,target_names):\n",
    "\n",
    "    dot_data = tree.export_graphviz(model, out_file=None, \n",
    "                                feature_names=feature_names,  \n",
    "                                class_names=target_names,\n",
    "                                filled=True)\n",
    "\n",
    "    # Draw graph\n",
    "    #graph = graphviz.Source(dot_data) \n",
    "    graph = graphviz.Source(dot_data,format='pdf')    \n",
    "    name=\"./tree/\"+name[18:-4]\n",
    "    graph.render(name, view=True)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from random import random\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./devicebasedcsvs\\\\Aria.csv',\n",
       " './devicebasedcsvs\\\\D-LinkCam.csv',\n",
       " './devicebasedcsvs\\\\D-LinkDayCam.csv',\n",
       " './devicebasedcsvs\\\\D-LinkDoorSensor.csv',\n",
       " './devicebasedcsvs\\\\D-LinkHomeHub.csv',\n",
       " './devicebasedcsvs\\\\D-LinkSensor.csv',\n",
       " './devicebasedcsvs\\\\D-LinkSiren.csv',\n",
       " './devicebasedcsvs\\\\D-LinkSwitch.csv',\n",
       " './devicebasedcsvs\\\\D-LinkWaterSensor.csv',\n",
       " './devicebasedcsvs\\\\EdimaxCam.csv',\n",
       " './devicebasedcsvs\\\\EdimaxPlug1101W.csv',\n",
       " './devicebasedcsvs\\\\EdimaxPlug2101W.csv',\n",
       " './devicebasedcsvs\\\\EdnetCam.csv',\n",
       " './devicebasedcsvs\\\\EdnetGateway.csv',\n",
       " './devicebasedcsvs\\\\HomeMaticPlug.csv',\n",
       " './devicebasedcsvs\\\\HueBridge.csv',\n",
       " './devicebasedcsvs\\\\HueSwitch.csv',\n",
       " './devicebasedcsvs\\\\IKettle2.csv',\n",
       " './devicebasedcsvs\\\\Lightify.csv',\n",
       " './devicebasedcsvs\\\\MAXGateway.csv',\n",
       " './devicebasedcsvs\\\\SmarterCoffee.csv',\n",
       " './devicebasedcsvs\\\\TP-LinkPlugHS100.csv',\n",
       " './devicebasedcsvs\\\\TP-LinkPlugHS110.csv',\n",
       " './devicebasedcsvs\\\\WeMoInsightSwitch.csv',\n",
       " './devicebasedcsvs\\\\WeMoLink.csv',\n",
       " './devicebasedcsvs\\\\WeMoSwitch.csv',\n",
       " './devicebasedcsvs\\\\Withings.csv']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_the_way(path,file_format):\n",
    "    files_add = []\n",
    "    # r=root, d=directories, f = files\n",
    "    for r, d, f in os.walk(path):\n",
    "        for file in f:\n",
    "            if file_format in file:\n",
    "                files_add.append(os.path.join(r, file))  \n",
    "    return files_add\n",
    "name_list=find_the_way('./devicebasedcsvs','.csv')\n",
    "name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_list={\"DT\":DecisionTreeClassifier()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def score(altime,train_time,test_time,predict,y_test,class_based_results,i,cv,dname,ii):\n",
    "    precision=[]\n",
    "    recall=[]\n",
    "    f1=[]\n",
    "    accuracy=[]\n",
    "    total_time=[]\n",
    "    kappa=[]\n",
    "    accuracy_b=[]\n",
    "    \n",
    "    rc=sklearn.metrics.recall_score(y_test, predict,average= \"macro\")\n",
    "    pr=sklearn.metrics.precision_score(y_test, predict,average= \"macro\")\n",
    "    f_1=sklearn.metrics.f1_score(y_test, predict,average= \"macro\")        \n",
    "    report = classification_report(y_test, predict, target_names=target_names,output_dict=True)\n",
    "    cr = pd.DataFrame(report).transpose()\n",
    "    if class_based_results.empty:\n",
    "        class_based_results =cr\n",
    "    else:\n",
    "        class_based_results = class_based_results.add(cr, fill_value=0)\n",
    "    precision.append(float(pr))\n",
    "    recall.append(float(rc))\n",
    "    f1.append(float(f_1))\n",
    "    accuracy_b.append(balanced_accuracy_score( y_test,predict))\n",
    "    accuracy.append(accuracy_score(y_test, predict))\n",
    "    #clf.score(X_test, y_test))\n",
    "    #print(balanced_accuracy_score( y_test,predict))\n",
    "    #t_time.append(float((time.time()-second)) )\n",
    "    kappa.append(round(float(sklearn.metrics.cohen_kappa_score(y_test, predict, \n",
    "    labels=None, weights=None, sample_weight=None)),15))\n",
    "    print ('%-15s %-3s %-3s %-6s  %-5s %-5s %-5s %-5s %-5s %-5s %-8s %-8s%-8s%-8s' % (dname,i,cv,ii[0:6],str(round(np.mean(accuracy),2)),str(round(np.mean(accuracy_b),2)),\n",
    "        str(round(np.mean(precision),2)), str(round(np.mean(recall),2)),str(round(np.mean(f1),2)), \n",
    "        str(round(np.mean(kappa),2)),str(round(np.mean(train_time),2)),str(round(np.mean(test_time),2)),str(round(np.mean(test_time)+np.mean(train_time),2)),str(round(np.mean(altime),2))))\n",
    "    lines=(str(dname)+\",\"+str(i)+\",\"+str(cv)+\",\"+str(ii)+\",\"+str(round(np.mean(accuracy),15))+\",\"+str(round(np.mean(accuracy_b),15))+\",\"+str(round(np.mean(precision),15))+\",\"+ str(round(np.mean(recall),15))+\",\"+str(round(np.mean(f1),15))+\",\"+str(round(np.mean(kappa),15))+\",\"+str(round(np.mean(train_time),15))+\",\"+str(round(np.mean(test_time),15))+\",\"+str(round(np.mean(test_time)+np.mean(train_time),15))+\",\"+str(altime)+\"\\n\")\n",
    "    return lines,class_based_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ML(loop1,loop2,output_csv,cols,step,Tree):\n",
    "    fold=10\n",
    "    ths = open(output_csv, \"w\")\n",
    "    ths.write(\"Dataset,T,CV,ML_algorithm,Acc,b_Acc,Precision, Recall , F1-score, kappa ,tra-Time,test-Time,total-Time,Al-Time\\n\")\n",
    "    repetition=1\n",
    "\n",
    "\n",
    "    from sklearn.metrics import balanced_accuracy_score\n",
    "    from sklearn.preprocessing import Normalizer\n",
    "    print ('%-15s %-3s %-3s %-6s  %-5s %-5s %-5s %-5s %-5s %-5s %-8s %-8s%-8s%-8s'%\n",
    "               (\"Dataset\",\"T\",\"CV\",\"ML_alg\",\"Acc\",\"b_Acc\",\"Prec\", \"Rec\" , \"F1\", \"kap\" ,\"tra-T\",\"test-T\",\"total\",\"al-time\"))\n",
    "\n",
    "    for ii in ml_list:\n",
    "        class_based_results=pd.DataFrame()#\"\" #pd.DataFrame(0, index=np.arange((len(target_names)+3)), columns=[\"f1-score\",\"precision\",\"recall\",\"support\"])\n",
    "        cm=pd.DataFrame()\n",
    "        cv=0\n",
    "        for i in range(repetition):\n",
    "            rnd = random()\n",
    "            kfold = sklearn.model_selection.KFold(n_splits=fold, shuffle=True, random_state=int(rnd*100))\n",
    "            \n",
    "            cv=0\n",
    "            df = pd.read_csv(loop1,usecols=cols)#,header=None )\n",
    "            #del df[\"MAC\"] # if dataset has MAC colomn please uncomment this line\n",
    "            feature_names=df.columns\n",
    "            feature_names=feature_names[0:-1]\n",
    "            X =df[df.columns[0:-1]]\n",
    "            X=np.array(X)\n",
    "            df[df.columns[-1]] = df[df.columns[-1]].astype('category')\n",
    "            y=df[df.columns[-1]].cat.codes  \n",
    "            #scaler = Normalizer().fit(X)\n",
    "            #X = scaler.transform(X)\n",
    "            # summarize transformed data\n",
    "            dname=loop1[18:-4]\n",
    "            X.shape\n",
    "            for train_index, test_index in kfold.split(X):\n",
    "\n",
    "                X_train, X_test = X[train_index], X[test_index]\n",
    "                y_train, y_test = y[train_index], y[test_index]  \n",
    "\n",
    "      \n",
    "\n",
    "\n",
    "                #dname=loop1  [6:-13]  \n",
    "                results_y=[]\n",
    "                \n",
    "                results_y.append(y_test)\n",
    "\n",
    "\n",
    "                precision=[]\n",
    "                recall=[]\n",
    "                f1=[]\n",
    "                accuracy=[]\n",
    "                train_time=[]\n",
    "                test_time=[]\n",
    "                total_time=[]\n",
    "                kappa=[]\n",
    "                accuracy_b=[]\n",
    "\n",
    "                #machine learning algorithm is applied in this section\n",
    "                clf = ml_list[ii]#choose algorithm from ml_list dictionary\n",
    "                second=time.time()\n",
    "                clf.fit(X_train, y_train)\n",
    "                train_time.append(float((time.time()-second)) )\n",
    "                second=time.time()\n",
    "                predict =clf.predict(X_test)\n",
    "                test_time.append(float((time.time()-second)) )\n",
    "                \n",
    "                \n",
    "                if Tree:\n",
    "                    if cv==1:# creates a decision tree for the first step of cross-validation.\n",
    "                        ciz(loop1,clf,feature_names,target_names)\n",
    "                cv+=1\n",
    "         \n",
    "                altime=0\n",
    "                lines,class_based_results=score(altime,train_time,test_time,predict,y_test,class_based_results,i,cv,dname,ii)\n",
    "\n",
    "                ths.write (lines)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "                df_cm = pd.DataFrame(confusion_matrix(y_test, predict))\n",
    "                results_y.append(predict)\n",
    "                if cm.empty:\n",
    "                    cm =df_cm\n",
    "                else:\n",
    "                    cm = cm.add(df_cm, fill_value=0)\n",
    "\n",
    "\n",
    "        print(class_based_results/(repetition*fold)) \n",
    "        graph_name=output_csv[:-4]+\".pdf\"     \n",
    "        plt.figure(figsize = (5,3.5))\n",
    "        sns.heatmap(cm,xticklabels=target_names, yticklabels=target_names,fmt='', annot=True)\n",
    "        #plt.savefig(graph_name,bbox_inches='tight')#, dpi=400)\n",
    "        plt.show()\n",
    "        #print(cm)\n",
    "        print(\"\\n\\n\\n\") \n",
    "    ths.close()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "features=['pck_size', 'Ether_type', 'LLC_dsap', 'LLC_ssap', 'LLC_ctrl',\n",
    "       'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_ihl',\n",
    "       'IP_tos', 'IP_len', 'IP_flags', 'IP_Z', 'IP_MF', 'IP_DF', 'IP_frag',\n",
    "       'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type',\n",
    "       'ICMP_code', 'ICMP_chksum', 'ICMP_id', 'ICMP_seq', 'ICMP_ts_ori',\n",
    "       'ICMP_ts_rx', 'ICMP_ts_tx', 'ICMP_ptr', 'ICMP_reserved', 'ICMP_length',\n",
    "        'ICMP_nexthopmtu', #'ICMP_unused',\n",
    "          'TCP_seq',\"IP_id\",\n",
    "       'TCP_ack', 'TCP_dataofs', 'TCP_reserved', 'TCP_flags', 'TCP_FIN',\n",
    "       'TCP_SYN', 'TCP_RST', 'TCP_PSH', 'TCP_ACK', 'TCP_URG', 'TCP_ECE',\n",
    "       'TCP_CWR', 'TCP_window', 'TCP_chksum', 'TCP_urgptr', 'TCP_options',\n",
    "       'UDP_len', 'UDP_chksum', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype',\n",
    "       'BOOTP_hlen', 'BOOTP_hops', 'BOOTP_xid', 'BOOTP_secs', 'BOOTP_flags',\n",
    "       'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_length', 'DNS_id',\n",
    "       'DNS_qr', 'DNS_opcode', 'DNS_aa', 'DNS_tc', 'DNS_rd', 'DNS_ra', 'DNS_z',\n",
    "       'DNS_ad', 'DNS_cd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount',\n",
    "       'DNS_nscount', 'DNS_arcount', 'sport_class', 'dport_class',\n",
    "       'sport_bare', 'dport_bare', 'TCP_sport', 'TCP_dport', 'UDP_sport',\n",
    "       'UDP_dport', 'payload_bytes', 'entropy', 'Protocol', 'Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "Aria            0   1   DT      1.0   1.0   0.99  1.0   0.99  0.98  0.0      0.0     0.0     0.0     \n",
      "Aria            0   2   DT      1.0   1.0   0.99  1.0   0.99  0.98  0.0      0.0     0.0     0.0     \n",
      "Aria            0   3   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "Aria            0   4   DT      1.0   0.99  1.0   0.99  0.99  0.98  0.02     0.0     0.02    0.0     \n",
      "Aria            0   5   DT      0.98  0.98  0.96  0.98  0.97  0.93  0.02     0.0     0.02    0.0     \n",
      "Aria            0   6   DT      0.99  0.97  0.98  0.97  0.98  0.95  0.02     0.0     0.02    0.0     \n",
      "Aria            0   7   DT      0.98  0.98  0.96  0.98  0.97  0.93  0.0      0.0     0.0     0.0     \n",
      "Aria            0   8   DT      0.99  0.99  0.97  0.99  0.98  0.96  0.0      0.0     0.0     0.0     \n",
      "Aria            0   9   DT      0.98  0.96  0.98  0.96  0.97  0.93  0.0      0.0     0.0     0.0     \n",
      "Aria            0   10  DT      1.0   1.0   1.0   1.0   1.0   1.0   0.01     0.0     0.01    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.995419  0.993168  0.994277  175.500000\n",
      "1              0.966721  0.978040  0.972000   35.100000\n",
      "accuracy       0.990499  0.990499  0.990499    0.990499\n",
      "macro avg      0.981070  0.985604  0.983139  210.600000\n",
      "weighted avg   0.990710  0.990499  0.990527  210.600000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "D-LinkCam       0   1   DT      0.96  0.9   0.94  0.9   0.92  0.84  0.08     0.0     0.08    0.0     \n",
      "D-LinkCam       0   2   DT      0.97  0.95  0.94  0.95  0.94  0.88  0.08     0.0     0.08    0.0     \n",
      "D-LinkCam       0   3   DT      0.95  0.91  0.92  0.91  0.91  0.82  0.06     0.0     0.06    0.0     \n",
      "D-LinkCam       0   4   DT      0.96  0.95  0.91  0.95  0.93  0.85  0.08     0.0     0.08    0.0     \n",
      "D-LinkCam       0   5   DT      0.97  0.95  0.93  0.95  0.94  0.88  0.09     0.0     0.09    0.0     \n",
      "D-LinkCam       0   6   DT      0.97  0.95  0.96  0.95  0.95  0.91  0.1      0.0     0.1     0.0     \n",
      "D-LinkCam       0   7   DT      0.97  0.94  0.96  0.94  0.95  0.9   0.08     0.0     0.08    0.0     \n",
      "D-LinkCam       0   8   DT      0.97  0.96  0.94  0.96  0.95  0.91  0.09     0.0     0.09    0.0     \n",
      "D-LinkCam       0   9   DT      0.96  0.93  0.94  0.93  0.93  0.87  0.09     0.0     0.09    0.0     \n",
      "D-LinkCam       0   10  DT      0.95  0.92  0.88  0.92  0.9   0.79  0.08     0.0     0.08    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.978981  0.976945  0.977921  500.000000\n",
      "1              0.883123  0.895282  0.888172  100.000000\n",
      "accuracy       0.963167  0.963167  0.963167    0.963167\n",
      "macro avg      0.931052  0.936114  0.933046  600.000000\n",
      "weighted avg   0.963796  0.963167  0.963283  600.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "D-LinkDayCam    0   1   DT      0.99  0.99  0.99  0.99  0.99  0.97  0.03     0.0     0.03    0.0     \n",
      "D-LinkDayCam    0   2   DT      0.98  0.96  0.97  0.96  0.97  0.93  0.02     0.0     0.02    0.0     \n",
      "D-LinkDayCam    0   3   DT      0.98  0.98  0.94  0.98  0.96  0.92  0.02     0.0     0.02    0.0     \n",
      "D-LinkDayCam    0   4   DT      0.98  0.98  0.97  0.98  0.97  0.95  0.03     0.0     0.03    0.0     \n",
      "D-LinkDayCam    0   5   DT      0.99  0.97  0.98  0.97  0.98  0.95  0.02     0.0     0.02    0.0     \n",
      "D-LinkDayCam    0   6   DT      0.98  0.98  0.96  0.98  0.97  0.94  0.02     0.0     0.02    0.0     \n",
      "D-LinkDayCam    0   7   DT      0.99  0.98  0.98  0.98  0.98  0.96  0.02     0.0     0.02    0.0     \n",
      "D-LinkDayCam    0   8   DT      0.99  0.98  0.97  0.98  0.97  0.95  0.03     0.0     0.03    0.0     \n",
      "D-LinkDayCam    0   9   DT      0.99  0.97  0.98  0.97  0.98  0.95  0.02     0.0     0.02    0.0     \n",
      "D-LinkDayCam    0   10  DT      0.99  0.97  0.97  0.97  0.97  0.94  0.02     0.0     0.02    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.992698  0.989637  0.991148  424.500000\n",
      "1              0.949461  0.963084  0.955857   84.900000\n",
      "accuracy       0.985278  0.985278  0.985278    0.985278\n",
      "macro avg      0.971080  0.976361  0.973502  509.400000\n",
      "weighted avg   0.985569  0.985278  0.985337  509.400000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "D-LinkDoorSensor 0   1   DT      0.97  0.96  0.95  0.96  0.95  0.9   0.06     0.0     0.06    0.0     \n",
      "D-LinkDoorSensor 0   2   DT      0.98  0.97  0.97  0.97  0.97  0.93  0.06     0.0     0.06    0.0     \n",
      "D-LinkDoorSensor 0   3   DT      0.98  0.97  0.96  0.97  0.97  0.94  0.05     0.0     0.05    0.0     \n",
      "D-LinkDoorSensor 0   4   DT      0.99  0.97  0.98  0.97  0.98  0.95  0.05     0.0     0.05    0.0     \n",
      "D-LinkDoorSensor 0   5   DT      0.98  0.97  0.94  0.97  0.95  0.91  0.07     0.0     0.07    0.0     \n",
      "D-LinkDoorSensor 0   6   DT      0.97  0.96  0.94  0.96  0.95  0.9   0.06     0.0     0.06    0.0     \n",
      "D-LinkDoorSensor 0   7   DT      0.99  0.98  0.98  0.98  0.98  0.95  0.06     0.0     0.06    0.0     \n",
      "D-LinkDoorSensor 0   8   DT      0.98  0.98  0.95  0.98  0.96  0.92  0.05     0.0     0.05    0.0     \n",
      "D-LinkDoorSensor 0   9   DT      0.97  0.96  0.94  0.96  0.95  0.9   0.06     0.0     0.06    0.0     \n",
      "D-LinkDoorSensor 0   10  DT      0.97  0.95  0.94  0.95  0.95  0.89  0.06     0.0     0.06    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.990119  0.982565  0.986310  500.000000\n",
      "1              0.917483  0.951081  0.933610  100.000000\n",
      "accuracy       0.977333  0.977333  0.977333    0.977333\n",
      "macro avg      0.953801  0.966823  0.959960  600.000000\n",
      "weighted avg   0.978002  0.977333  0.977529  600.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "D-LinkHomeHub   0   1   DT      0.93  0.89  0.88  0.89  0.89  0.77  0.1      0.0     0.1     0.0     \n",
      "D-LinkHomeHub   0   2   DT      0.91  0.81  0.85  0.81  0.83  0.65  0.09     0.0     0.09    0.0     \n",
      "D-LinkHomeHub   0   3   DT      0.92  0.85  0.87  0.85  0.86  0.72  0.06     0.0     0.06    0.0     \n",
      "D-LinkHomeHub   0   4   DT      0.93  0.89  0.86  0.89  0.88  0.75  0.09     0.0     0.09    0.0     \n",
      "D-LinkHomeHub   0   5   DT      0.93  0.87  0.88  0.87  0.88  0.75  0.09     0.0     0.09    0.0     \n",
      "D-LinkHomeHub   0   6   DT      0.92  0.86  0.85  0.86  0.85  0.71  0.08     0.0     0.08    0.0     \n",
      "D-LinkHomeHub   0   7   DT      0.92  0.87  0.86  0.87  0.86  0.73  0.08     0.0     0.08    0.0     \n",
      "D-LinkHomeHub   0   8   DT      0.92  0.86  0.84  0.86  0.85  0.71  0.08     0.0     0.08    0.0     \n",
      "D-LinkHomeHub   0   9   DT      0.94  0.89  0.87  0.89  0.88  0.76  0.06     0.0     0.06    0.0     \n",
      "D-LinkHomeHub   0   10  DT      0.93  0.89  0.87  0.89  0.88  0.76  0.08     0.02    0.09    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.956751  0.953614  0.955137  500.000000\n",
      "1              0.771802  0.784232  0.777091  100.000000\n",
      "accuracy       0.925333  0.925333  0.925333    0.925333\n",
      "macro avg      0.864276  0.868923  0.866114  600.000000\n",
      "weighted avg   0.926034  0.925333  0.925493  600.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "D-LinkSensor    0   1   DT      0.93  0.89  0.87  0.89  0.88  0.76  0.06     0.0     0.06    0.0     \n",
      "D-LinkSensor    0   2   DT      0.94  0.92  0.87  0.92  0.89  0.78  0.06     0.0     0.06    0.0     \n",
      "D-LinkSensor    0   3   DT      0.94  0.88  0.89  0.88  0.89  0.77  0.08     0.0     0.08    0.0     \n",
      "D-LinkSensor    0   4   DT      0.94  0.92  0.88  0.92  0.9   0.8   0.05     0.0     0.05    0.0     \n",
      "D-LinkSensor    0   5   DT      0.94  0.87  0.92  0.87  0.89  0.78  0.08     0.0     0.08    0.0     \n",
      "D-LinkSensor    0   6   DT      0.94  0.91  0.87  0.91  0.89  0.78  0.08     0.0     0.08    0.0     \n",
      "D-LinkSensor    0   7   DT      0.94  0.88  0.88  0.88  0.88  0.77  0.08     0.0     0.08    0.0     \n",
      "D-LinkSensor    0   8   DT      0.93  0.91  0.86  0.91  0.88  0.77  0.06     0.0     0.06    0.0     \n",
      "D-LinkSensor    0   9   DT      0.94  0.9   0.89  0.9   0.89  0.78  0.06     0.0     0.06    0.0     \n",
      "D-LinkSensor    0   10  DT      0.94  0.88  0.91  0.88  0.89  0.79  0.07     0.0     0.07    0.0     \n",
      "              precision    recall  f1-score  support\n",
      "0              0.966001  0.958272  0.962011  500.000\n",
      "1              0.801213  0.833259  0.814748  100.000\n",
      "accuracy       0.937000  0.937000  0.937000    0.937\n",
      "macro avg      0.883607  0.895765  0.888379  600.000\n",
      "weighted avg   0.939044  0.937000  0.937500  600.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "D-LinkSiren     0   1   DT      0.92  0.84  0.87  0.84  0.85  0.71  0.08     0.0     0.08    0.0     \n",
      "D-LinkSiren     0   2   DT      0.93  0.86  0.88  0.86  0.87  0.74  0.06     0.0     0.06    0.0     \n",
      "D-LinkSiren     0   3   DT      0.94  0.88  0.9   0.88  0.89  0.77  0.05     0.0     0.05    0.0     \n",
      "D-LinkSiren     0   4   DT      0.94  0.87  0.87  0.87  0.87  0.74  0.05     0.0     0.05    0.0     \n",
      "D-LinkSiren     0   5   DT      0.94  0.86  0.91  0.86  0.89  0.77  0.06     0.0     0.06    0.0     \n",
      "D-LinkSiren     0   6   DT      0.93  0.87  0.88  0.87  0.88  0.76  0.06     0.0     0.06    0.0     \n",
      "D-LinkSiren     0   7   DT      0.93  0.86  0.9   0.86  0.88  0.75  0.06     0.0     0.06    0.0     \n",
      "D-LinkSiren     0   8   DT      0.92  0.84  0.9   0.84  0.86  0.73  0.05     0.0     0.05    0.0     \n",
      "D-LinkSiren     0   9   DT      0.9   0.81  0.81  0.81  0.81  0.62  0.06     0.0     0.06    0.0     \n",
      "D-LinkSiren     0   10  DT      0.92  0.84  0.85  0.84  0.84  0.69  0.06     0.0     0.06    0.0     \n",
      "              precision    recall  f1-score  support\n",
      "0              0.948806  0.964469  0.956526  500.000\n",
      "1              0.806674  0.740845  0.771422  100.000\n",
      "accuracy       0.927000  0.927000  0.927000    0.927\n",
      "macro avg      0.877740  0.852657  0.863974  600.000\n",
      "weighted avg   0.925643  0.927000  0.925812  600.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "D-LinkSwitch    0   1   DT      0.94  0.89  0.89  0.89  0.89  0.78  0.09     0.0     0.09    0.0     \n",
      "D-LinkSwitch    0   2   DT      0.95  0.92  0.91  0.92  0.91  0.83  0.08     0.0     0.08    0.0     \n",
      "D-LinkSwitch    0   3   DT      0.95  0.92  0.89  0.92  0.9   0.81  0.07     0.0     0.07    0.0     \n",
      "D-LinkSwitch    0   4   DT      0.96  0.93  0.93  0.93  0.93  0.85  0.06     0.0     0.06    0.0     \n",
      "D-LinkSwitch    0   5   DT      0.94  0.9   0.89  0.9   0.89  0.79  0.06     0.02    0.08    0.0     \n",
      "D-LinkSwitch    0   6   DT      0.94  0.91  0.9   0.91  0.9   0.8   0.06     0.01    0.08    0.0     \n",
      "D-LinkSwitch    0   7   DT      0.94  0.89  0.88  0.89  0.89  0.78  0.06     0.02    0.08    0.0     \n",
      "D-LinkSwitch    0   8   DT      0.95  0.9   0.92  0.9   0.91  0.82  0.08     0.0     0.08    0.0     \n",
      "D-LinkSwitch    0   9   DT      0.94  0.9   0.91  0.9   0.9   0.81  0.08     0.0     0.08    0.0     \n",
      "D-LinkSwitch    0   10  DT      0.95  0.91  0.89  0.91  0.9   0.8   0.07     0.0     0.07    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.969296  0.966058  0.967659  500.000000\n",
      "1              0.831726  0.846819  0.838882  100.000000\n",
      "accuracy       0.946167  0.946167  0.946167    0.946167\n",
      "macro avg      0.900511  0.906438  0.903270  600.000000\n",
      "weighted avg   0.946735  0.946167  0.946373  600.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "D-LinkWaterSensor 0   1   DT      0.93  0.88  0.87  0.88  0.87  0.74  0.08     0.0     0.08    0.0     \n",
      "D-LinkWaterSensor 0   2   DT      0.94  0.92  0.89  0.92  0.9   0.8   0.06     0.0     0.06    0.0     \n",
      "D-LinkWaterSensor 0   3   DT      0.95  0.91  0.89  0.91  0.9   0.8   0.06     0.0     0.06    0.0     \n",
      "D-LinkWaterSensor 0   4   DT      0.93  0.87  0.88  0.87  0.87  0.74  0.08     0.0     0.08    0.0     \n",
      "D-LinkWaterSensor 0   5   DT      0.95  0.91  0.89  0.91  0.9   0.8   0.08     0.0     0.08    0.0     \n",
      "D-LinkWaterSensor 0   6   DT      0.94  0.89  0.9   0.89  0.89  0.79  0.06     0.0     0.06    0.0     \n",
      "D-LinkWaterSensor 0   7   DT      0.94  0.91  0.88  0.91  0.89  0.79  0.06     0.02    0.08    0.0     \n",
      "D-LinkWaterSensor 0   8   DT      0.95  0.92  0.91  0.92  0.91  0.82  0.08     0.0     0.08    0.0     \n",
      "D-LinkWaterSensor 0   9   DT      0.94  0.89  0.9   0.89  0.89  0.79  0.06     0.0     0.06    0.0     \n",
      "D-LinkWaterSensor 0   10  DT      0.95  0.91  0.91  0.91  0.91  0.82  0.06     0.0     0.06    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.967027  0.961814  0.964377  500.000000\n",
      "1              0.813696  0.836576  0.824308  100.000000\n",
      "accuracy       0.940833  0.940833  0.940833    0.940833\n",
      "macro avg      0.890362  0.899195  0.894343  600.000000\n",
      "weighted avg   0.941787  0.940833  0.941135  600.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "EdimaxCam       0   1   DT      0.99  0.98  0.97  0.98  0.98  0.96  0.02     0.0     0.02    0.0     \n",
      "EdimaxCam       0   2   DT      0.98  0.96  0.97  0.96  0.96  0.93  0.02     0.0     0.02    0.0     \n",
      "EdimaxCam       0   3   DT      0.98  0.99  0.95  0.99  0.97  0.93  0.03     0.0     0.03    0.0     \n",
      "EdimaxCam       0   4   DT      0.98  0.96  0.96  0.96  0.96  0.92  0.03     0.0     0.03    0.0     \n",
      "EdimaxCam       0   5   DT      0.98  0.98  0.96  0.98  0.97  0.94  0.02     0.0     0.02    0.0     \n",
      "EdimaxCam       0   6   DT      0.98  0.97  0.96  0.97  0.96  0.93  0.02     0.0     0.02    0.0     \n",
      "EdimaxCam       0   7   DT      0.98  0.97  0.96  0.97  0.96  0.92  0.02     0.0     0.02    0.0     \n",
      "EdimaxCam       0   8   DT      0.97  0.96  0.94  0.96  0.95  0.9   0.02     0.0     0.02    0.0     \n",
      "EdimaxCam       0   9   DT      0.98  0.98  0.97  0.98  0.97  0.94  0.03     0.0     0.03    0.0     \n",
      "EdimaxCam       0   10  DT      0.98  0.98  0.97  0.98  0.97  0.95  0.02     0.0     0.02    0.0     \n",
      "              precision    recall  f1-score    support\n",
      "0              0.991812  0.985279  0.988527  333.00000\n",
      "1              0.928154  0.960294  0.943672   66.60000\n",
      "accuracy       0.980980  0.980980  0.980980    0.98098\n",
      "macro avg      0.959983  0.972787  0.966099  399.60000\n",
      "weighted avg   0.981421  0.980980  0.981105  399.60000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "EdimaxPlug1101W 0   1   DT      0.98  0.97  0.96  0.97  0.96  0.93  0.05     0.0     0.05    0.0     \n",
      "EdimaxPlug1101W 0   2   DT      0.98  0.96  0.97  0.96  0.97  0.93  0.06     0.0     0.06    0.0     \n",
      "EdimaxPlug1101W 0   3   DT      0.98  0.96  0.96  0.96  0.96  0.92  0.05     0.0     0.05    0.0     \n",
      "EdimaxPlug1101W 0   4   DT      0.98  0.97  0.96  0.97  0.96  0.93  0.03     0.0     0.03    0.0     \n",
      "EdimaxPlug1101W 0   5   DT      0.99  0.98  0.97  0.98  0.97  0.95  0.05     0.0     0.05    0.0     \n",
      "EdimaxPlug1101W 0   6   DT      0.98  0.97  0.96  0.97  0.97  0.94  0.06     0.0     0.06    0.0     \n",
      "EdimaxPlug1101W 0   7   DT      0.99  0.98  0.97  0.98  0.98  0.95  0.05     0.0     0.05    0.0     \n",
      "EdimaxPlug1101W 0   8   DT      0.99  0.98  0.97  0.98  0.97  0.95  0.05     0.0     0.05    0.0     \n",
      "EdimaxPlug1101W 0   9   DT      0.99  0.97  0.98  0.97  0.98  0.95  0.05     0.0     0.05    0.0     \n",
      "EdimaxPlug1101W 0   10  DT      0.99  0.99  0.96  0.99  0.97  0.94  0.05     0.0     0.05    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.991917  0.987236  0.989564  448.000000\n",
      "1              0.938383  0.960026  0.948931   89.600000\n",
      "accuracy       0.982704  0.982704  0.982704    0.982704\n",
      "macro avg      0.965150  0.973631  0.969248  537.600000\n",
      "weighted avg   0.982966  0.982704  0.982780  537.600000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "EdimaxPlug2101W 0   1   DT      0.98  0.98  0.95  0.98  0.96  0.93  0.05     0.0     0.05    0.0     \n",
      "EdimaxPlug2101W 0   2   DT      0.98  0.97  0.94  0.97  0.96  0.91  0.05     0.0     0.05    0.0     \n",
      "EdimaxPlug2101W 0   3   DT      0.98  0.98  0.96  0.98  0.97  0.94  0.03     0.0     0.03    0.0     \n",
      "EdimaxPlug2101W 0   4   DT      0.98  0.97  0.96  0.97  0.97  0.93  0.06     0.0     0.06    0.0     \n",
      "EdimaxPlug2101W 0   5   DT      0.98  0.98  0.96  0.98  0.97  0.93  0.05     0.0     0.05    0.0     \n",
      "EdimaxPlug2101W 0   6   DT      0.98  0.97  0.98  0.97  0.97  0.95  0.05     0.0     0.05    0.0     \n",
      "EdimaxPlug2101W 0   7   DT      0.98  0.96  0.97  0.96  0.97  0.93  0.05     0.0     0.05    0.0     \n",
      "EdimaxPlug2101W 0   8   DT      0.98  0.97  0.98  0.97  0.97  0.95  0.04     0.01    0.05    0.0     \n",
      "EdimaxPlug2101W 0   9   DT      0.98  0.97  0.96  0.97  0.96  0.92  0.05     0.0     0.05    0.0     \n",
      "EdimaxPlug2101W 0   10  DT      0.98  0.97  0.95  0.97  0.96  0.92  0.05     0.0     0.05    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.991825  0.984816  0.988295  382.000000\n",
      "1              0.926386  0.959455  0.942330   76.400000\n",
      "accuracy       0.980586  0.980586  0.980586    0.980586\n",
      "macro avg      0.959105  0.972136  0.965312  458.400000\n",
      "weighted avg   0.981116  0.980586  0.980735  458.400000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "EdnetCam        0   1   DT      0.98  0.97  0.97  0.97  0.97  0.93  0.02     0.0     0.02    0.0     \n",
      "EdnetCam        0   2   DT      0.96  0.95  0.92  0.95  0.93  0.87  0.02     0.0     0.02    0.0     \n",
      "EdnetCam        0   3   DT      0.98  0.99  0.95  0.99  0.97  0.94  0.02     0.0     0.02    0.0     \n",
      "EdnetCam        0   4   DT      0.97  0.95  0.96  0.95  0.96  0.91  0.0      0.0     0.0     0.0     \n",
      "EdnetCam        0   5   DT      0.98  0.98  0.97  0.98  0.97  0.94  0.0      0.0     0.0     0.0     \n",
      "EdnetCam        0   6   DT      0.98  0.97  0.97  0.97  0.97  0.94  0.02     0.0     0.02    0.0     \n",
      "EdnetCam        0   7   DT      0.97  0.95  0.96  0.95  0.96  0.91  0.02     0.0     0.02    0.0     \n",
      "EdnetCam        0   8   DT      0.98  0.98  0.97  0.98  0.97  0.95  0.0      0.0     0.0     0.0     \n",
      "EdnetCam        0   9   DT      0.98  0.96  0.98  0.96  0.97  0.94  0.0      0.0     0.0     0.0     \n",
      "EdnetCam        0   10  DT      0.97  0.94  0.96  0.94  0.95  0.9   0.0      0.0     0.0     0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.988471  0.986079  0.987254  165.000000\n",
      "1              0.930888  0.942093  0.935932   33.000000\n",
      "accuracy       0.978788  0.978788  0.978788    0.978788\n",
      "macro avg      0.959679  0.964086  0.961593  198.000000\n",
      "weighted avg   0.979109  0.978788  0.978844  198.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "EdnetGateway    0   1   DT      0.99  0.99  0.98  0.99  0.98  0.96  0.02     0.0     0.02    0.0     \n",
      "EdnetGateway    0   2   DT      0.99  0.99  0.98  0.99  0.98  0.97  0.02     0.0     0.02    0.0     \n",
      "EdnetGateway    0   3   DT      0.98  0.97  0.95  0.97  0.96  0.93  0.02     0.0     0.02    0.0     \n",
      "EdnetGateway    0   4   DT      0.99  0.97  0.99  0.97  0.98  0.96  0.02     0.0     0.02    0.0     \n",
      "EdnetGateway    0   5   DT      0.99  0.99  0.99  0.99  0.99  0.98  0.02     0.0     0.02    0.0     \n",
      "EdnetGateway    0   6   DT      0.98  0.97  0.98  0.97  0.97  0.95  0.02     0.0     0.02    0.0     \n",
      "EdnetGateway    0   7   DT      0.98  0.98  0.96  0.98  0.97  0.94  0.02     0.0     0.02    0.0     \n",
      "EdnetGateway    0   8   DT      0.99  0.97  0.99  0.97  0.98  0.97  0.0      0.0     0.0     0.0     \n",
      "EdnetGateway    0   9   DT      1.0   0.99  1.0   0.99  0.99  0.99  0.02     0.0     0.02    0.0     \n",
      "EdnetGateway    0   10  DT      1.0   1.0   0.99  1.0   0.99  0.99  0.02     0.0     0.02    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.993360  0.993751  0.993540  273.000000\n",
      "1              0.969963  0.968376  0.968860   54.600000\n",
      "accuracy       0.989319  0.989319  0.989319    0.989319\n",
      "macro avg      0.981661  0.981063  0.981200  327.600000\n",
      "weighted avg   0.989471  0.989319  0.989328  327.600000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "HomeMaticPlug   0   1   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "HomeMaticPlug   0   2   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.01     0.0     0.01    0.0     \n",
      "HomeMaticPlug   0   3   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "HomeMaticPlug   0   4   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.01     0.0     0.01    0.0     \n",
      "HomeMaticPlug   0   5   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "HomeMaticPlug   0   6   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.02     0.0     0.02    0.0     \n",
      "HomeMaticPlug   0   7   DT      1.0   1.0   0.99  1.0   0.99  0.98  0.0      0.0     0.0     0.0     \n",
      "HomeMaticPlug   0   8   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "HomeMaticPlug   0   9   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.02     0.0     0.02    0.0     \n",
      "HomeMaticPlug   0   10  DT      1.0   1.0   1.0   1.0   1.0   1.0   0.02     0.0     0.02    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              1.000000  0.999628  0.999814  253.500000\n",
      "1              0.997222  1.000000  0.998592   50.700000\n",
      "accuracy       0.999671  0.999671  0.999671    0.999671\n",
      "macro avg      0.998611  0.999814  0.999203  304.200000\n",
      "weighted avg   0.999680  0.999671  0.999673  304.200000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "HueBridge       0   1   DT      0.93  0.86  0.89  0.86  0.87  0.75  0.11     0.0     0.11    0.0     \n",
      "HueBridge       0   2   DT      0.93  0.88  0.88  0.88  0.88  0.76  0.09     0.0     0.09    0.0     \n",
      "HueBridge       0   3   DT      0.92  0.86  0.86  0.86  0.86  0.73  0.11     0.0     0.11    0.0     \n",
      "HueBridge       0   4   DT      0.92  0.87  0.86  0.87  0.87  0.74  0.1      0.0     0.1     0.0     \n",
      "HueBridge       0   5   DT      0.93  0.87  0.86  0.87  0.87  0.73  0.09     0.0     0.09    0.0     \n",
      "HueBridge       0   6   DT      0.92  0.86  0.87  0.86  0.87  0.73  0.1      0.0     0.1     0.0     \n",
      "HueBridge       0   7   DT      0.92  0.84  0.85  0.84  0.85  0.69  0.09     0.0     0.09    0.0     \n",
      "HueBridge       0   8   DT      0.92  0.87  0.87  0.87  0.87  0.73  0.1      0.0     0.1     0.0     \n",
      "HueBridge       0   9   DT      0.96  0.91  0.92  0.91  0.91  0.82  0.09     0.0     0.09    0.0     \n",
      "HueBridge       0   10  DT      0.92  0.88  0.84  0.88  0.86  0.71  0.12     0.0     0.12    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.956557  0.956523  0.956511  500.000000\n",
      "1              0.784605  0.782903  0.783194  100.000000\n",
      "accuracy       0.927667  0.927667  0.927667    0.927667\n",
      "macro avg      0.870581  0.869713  0.869853  600.000000\n",
      "weighted avg   0.927935  0.927667  0.927684  600.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "HueSwitch       0   1   DT      0.95  0.9   0.91  0.9   0.9   0.8   0.06     0.0     0.06    0.0     \n",
      "HueSwitch       0   2   DT      0.92  0.87  0.86  0.87  0.87  0.73  0.05     0.0     0.05    0.0     \n",
      "HueSwitch       0   3   DT      0.94  0.89  0.89  0.89  0.89  0.79  0.06     0.0     0.06    0.0     \n",
      "HueSwitch       0   4   DT      0.93  0.86  0.86  0.86  0.86  0.73  0.06     0.0     0.06    0.0     \n",
      "HueSwitch       0   5   DT      0.95  0.9   0.93  0.9   0.91  0.83  0.05     0.0     0.05    0.0     \n",
      "HueSwitch       0   6   DT      0.94  0.88  0.92  0.88  0.9   0.8   0.06     0.0     0.06    0.0     \n",
      "HueSwitch       0   7   DT      0.94  0.9   0.9   0.9   0.9   0.8   0.06     0.0     0.06    0.0     \n",
      "HueSwitch       0   8   DT      0.95  0.92  0.88  0.92  0.9   0.8   0.05     0.0     0.05    0.0     \n",
      "HueSwitch       0   9   DT      0.95  0.91  0.9   0.91  0.9   0.8   0.06     0.0     0.06    0.0     \n",
      "HueSwitch       0   10  DT      0.94  0.91  0.88  0.91  0.89  0.79  0.06     0.02    0.07    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.964956  0.964010  0.964430  500.000000\n",
      "1              0.821526  0.825202  0.822327  100.000000\n",
      "accuracy       0.940833  0.940833  0.940833    0.940833\n",
      "macro avg      0.893241  0.894606  0.893379  600.000000\n",
      "weighted avg   0.941473  0.940833  0.940927  600.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "IKettle2        0   1   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "IKettle2        0   2   DT      0.99  0.99  0.95  0.99  0.97  0.94  0.0      0.0     0.0     0.0     \n",
      "IKettle2        0   3   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.02    0.02    0.0     \n",
      "IKettle2        0   4   DT      0.99  0.99  0.96  0.99  0.97  0.95  0.0      0.0     0.0     0.0     \n",
      "IKettle2        0   5   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "IKettle2        0   6   DT      0.97  0.98  0.93  0.98  0.96  0.91  0.01     0.0     0.01    0.0     \n",
      "IKettle2        0   7   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "IKettle2        0   8   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "IKettle2        0   9   DT      0.97  0.9   0.98  0.9   0.94  0.87  0.0      0.0     0.0     0.0     \n",
      "IKettle2        0   10  DT      0.96  0.95  0.93  0.95  0.94  0.88  0.0      0.0     0.0     0.0     \n",
      "              precision    recall  f1-score    support\n",
      "0              0.994887  0.989453  0.992090  58.500000\n",
      "1              0.957478  0.973750  0.963545  11.700000\n",
      "accuracy       0.987163  0.987163  0.987163   0.987163\n",
      "macro avg      0.976183  0.981602  0.977818  70.200000\n",
      "weighted avg   0.988024  0.987163  0.987202  70.200000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "Lightify        0   1   DT      1.0   0.99  0.99  0.99  0.99  0.98  0.03     0.0     0.03    0.0     \n",
      "Lightify        0   2   DT      0.99  0.98  0.99  0.98  0.98  0.97  0.02     0.0     0.02    0.0     \n",
      "Lightify        0   3   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.03     0.0     0.03    0.0     \n",
      "Lightify        0   4   DT      1.0   0.99  0.99  0.99  0.99  0.98  0.02     0.0     0.02    0.0     \n",
      "Lightify        0   5   DT      0.99  0.99  0.99  0.99  0.99  0.97  0.03     0.0     0.03    0.0     \n",
      "Lightify        0   6   DT      0.99  0.98  0.98  0.98  0.98  0.96  0.02     0.0     0.02    0.0     \n",
      "Lightify        0   7   DT      1.0   1.0   1.0   1.0   1.0   0.99  0.02     0.0     0.02    0.0     \n",
      "Lightify        0   8   DT      0.99  0.98  0.99  0.98  0.99  0.97  0.02     0.0     0.02    0.0     \n",
      "Lightify        0   9   DT      1.0   0.99  1.0   0.99  0.99  0.99  0.03     0.0     0.03    0.0     \n",
      "Lightify        0   10  DT      0.99  0.99  0.99  0.99  0.99  0.97  0.02     0.0     0.02    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.996413  0.997209  0.996809  500.000000\n",
      "1              0.985663  0.981707  0.983637  100.000000\n",
      "accuracy       0.994667  0.994667  0.994667    0.994667\n",
      "macro avg      0.991038  0.989458  0.990223  600.000000\n",
      "weighted avg   0.994673  0.994667  0.994660  600.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "MAXGateway      0   1   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "MAXGateway      0   2   DT      1.0   1.0   0.99  1.0   0.99  0.99  0.02     0.0     0.02    0.0     \n",
      "MAXGateway      0   3   DT      0.99  1.0   0.98  1.0   0.99  0.98  0.02     0.0     0.02    0.0     \n",
      "MAXGateway      0   4   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.02     0.0     0.02    0.0     \n",
      "MAXGateway      0   5   DT      1.0   1.0   0.99  1.0   0.99  0.99  0.02     0.0     0.02    0.0     \n",
      "MAXGateway      0   6   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.02     0.0     0.02    0.0     \n",
      "MAXGateway      0   7   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.01     0.0     0.01    0.0     \n",
      "MAXGateway      0   8   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.02     0.0     0.02    0.0     \n",
      "MAXGateway      0   9   DT      1.0   0.99  1.0   0.99  0.99  0.99  0.02     0.0     0.02    0.0     \n",
      "MAXGateway      0   10  DT      1.0   1.0   1.0   1.0   1.0   1.0   0.02     0.0     0.02    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.999545  0.998208  0.998873  225.500000\n",
      "1              0.991852  0.998039  0.994875   45.100000\n",
      "accuracy       0.998154  0.998154  0.998154    0.998154\n",
      "macro avg      0.995699  0.998124  0.996874  270.600000\n",
      "weighted avg   0.998200  0.998154  0.998161  270.600000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "SmarterCoffee   0   1   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "SmarterCoffee   0   2   DT      0.99  0.99  0.96  0.99  0.98  0.95  0.02     0.0     0.02    0.0     \n",
      "SmarterCoffee   0   3   DT      0.99  0.99  0.97  0.99  0.98  0.96  0.0      0.0     0.0     0.0     \n",
      "SmarterCoffee   0   4   DT      0.99  0.99  0.95  0.99  0.97  0.94  0.0      0.0     0.0     0.0     \n",
      "SmarterCoffee   0   5   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "SmarterCoffee   0   6   DT      0.95  0.9   0.96  0.9   0.93  0.86  0.01     0.0     0.01    0.0     \n",
      "SmarterCoffee   0   7   DT      0.99  0.99  0.94  0.99  0.97  0.93  0.0      0.0     0.0     0.0     \n",
      "SmarterCoffee   0   8   DT      0.99  0.94  0.99  0.94  0.96  0.93  0.0      0.0     0.0     0.0     \n",
      "SmarterCoffee   0   9   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "SmarterCoffee   0   10  DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "              precision    recall  f1-score    support\n",
      "0              0.991319  0.993512  0.992250  60.500000\n",
      "1              0.964947  0.968452  0.964436  12.100000\n",
      "accuracy       0.987633  0.987633  0.987633   0.987633\n",
      "macro avg      0.978133  0.980982  0.978343  72.600000\n",
      "weighted avg   0.988528  0.987633  0.987509  72.600000\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZxCv/9wrfvP2bND7eyKL/WlR11153uVxt3JmR12XtnYiIqRFxZEQc+UYNXADPPbcGgBdeWM+sWXN43/sOr7ZD1mXrVq9j8YLFvLThJV754yssfGAhB/7lgVV3qxK5jLw6DF5pHem2tsd5bQGxN6U999yDvffe69X9E0/4ME888XTFvbKueuQ/H2HkwSPZbffd6NW7F+96/7v4/dLfV92tSrRElNqq1lnaOAT4GLD95TQB/90tPcrEkCGDueOn0wDo06c3t9/+M+77+YPVdsoKLv7+xRw2+jD2GbgPtzx0C7d85xa2bNrC+d84n34D+3HZTZex7MllXPrZS9myaQt3/uhOrrn7GoJg4f0LWXj/wqp/hUpUH5bKUXQQQSVNA25MT4dvf+7HEfGZzn5An12H5fLfwrZz4pDDqu6C7YQ5K+Z0aVnBM992Wqm/2RnLf1bp62k7HHlFxMQOznUauMwsP7lM2Ps+LzMryOU+LwcvMyvoCVcSy3DwMrOCjubBexIHLzMrcNpoZlly2mhmWfLIy8yy5DkvM8tSLmmjl8Qxs4I6vjH7BklrJS2uKRsoaa6kpelzQCqXpGslNabnp4/orH0HLzMriIhSWwk3ASdtVzYFmBcRo4B56RhgLDAqbQ3AdZ017uBlZgXNtJTaOhMRvwRe3K54PDA97U8HTqspvzlazQf6SxraUfsOXmZWUHZJHEkNkh6u2RpKND8kIlan/TW8trTWMKB2je6VqaxdnrA3s4Ky0/URMRWY2uWfExGSunx1wMHLzAqaunddieclDY2I1SktXJvKVwEjauoNT2XtctpoZgV1nLBvy2xgQtqfAMyqKT87XXUcDWyqSS/b5JGXmRXU6w57STOA44B9Ja0E/h64EpgpaSKwHDgjVb8HGAc0Ai8D53TWvoOXmRW0RH3Sxog4s51TY9qoG8AFO9K+g5eZFfjZRjPLkp9tNLMslbkBtSdw8DKzgp7wTsYyHLzMrKC5ThP23c3By8wKwhP2ZpYjp41mliWnjWaWJaeNZpal8MjLzHLktNHMsuTHg8wsS348yMyy5LTRzLLkkZeZZclzXmaWpeYWp41mliGnjWaWJaeNZpYlp41mliU/22hmWfKSOGaWpXq9+qy7OXiZWYGvNppZlnIJXsqloz2VpIaImFp1P6xr/O+Xr15Vd+ANoKHqDthO8b9fphy8zCxLDl5mliUHr53n+ZK8+d8vU56wN7MseeRlZlly8NoJkk6S9LSkRklTqu6PlSfpBklrJS2uui/WNQ5eXSSpN/ADYCxwCHCmpEOq7ZXtgJuAk6ruhHWdg1fXHQU0RsSyiPgTcDswvuI+WUkR8Uvgxar7YV3n4NV1w4AVNccrU5mZvQ4cvMwsSw5eXbcKGFFzPDyVmdnrwMGr6xYCoyQdIGlX4NPA7Ir7ZPam4eDVRRHRBHwBuA9YAsyMiCeq7ZWVJWkG8BvgIEkrJU2suk+2Y3yHvZllySMvM8uSg5eZZcnBy8yy5OBlZlly8DKzLDl4mVmWHLzMLEsOXmaWpf8HVr9v/t5nQW4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "TP-LinkPlugHS100 0   1   DT      0.98  0.98  0.96  0.98  0.97  0.94  0.02     0.0     0.02    0.0     \n",
      "TP-LinkPlugHS100 0   2   DT      0.97  0.95  0.93  0.95  0.94  0.87  0.03     0.0     0.03    0.0     \n",
      "TP-LinkPlugHS100 0   3   DT      0.98  0.97  0.97  0.97  0.97  0.94  0.03     0.0     0.03    0.0     \n",
      "TP-LinkPlugHS100 0   4   DT      0.98  0.96  0.95  0.96  0.96  0.91  0.02     0.02    0.03    0.0     \n",
      "TP-LinkPlugHS100 0   5   DT      0.97  0.95  0.94  0.95  0.94  0.89  0.02     0.0     0.02    0.0     \n",
      "TP-LinkPlugHS100 0   6   DT      0.98  0.96  0.96  0.96  0.96  0.92  0.03     0.0     0.03    0.0     \n",
      "TP-LinkPlugHS100 0   7   DT      0.98  0.95  0.96  0.95  0.96  0.91  0.01     0.0     0.01    0.0     \n",
      "TP-LinkPlugHS100 0   8   DT      0.99  0.98  0.98  0.98  0.98  0.96  0.02     0.0     0.02    0.0     \n",
      "TP-LinkPlugHS100 0   9   DT      0.99  0.99  0.98  0.99  0.98  0.96  0.03     0.0     0.03    0.0     \n",
      "TP-LinkPlugHS100 0   10  DT      0.98  0.97  0.97  0.97  0.97  0.94  0.02     0.0     0.02    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.989266  0.985431  0.987338  262.000000\n",
      "1              0.929530  0.945948  0.937531   52.400000\n",
      "accuracy       0.979011  0.979011  0.979011    0.979011\n",
      "macro avg      0.959398  0.965690  0.962435  314.400000\n",
      "weighted avg   0.979279  0.979011  0.979101  314.400000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "TP-LinkPlugHS110 0   1   DT      0.97  0.97  0.93  0.97  0.95  0.9   0.03     0.0     0.03    0.0     \n",
      "TP-LinkPlugHS110 0   2   DT      0.99  0.99  0.96  0.99  0.98  0.95  0.02     0.0     0.02    0.0     \n",
      "TP-LinkPlugHS110 0   3   DT      0.98  0.95  0.98  0.95  0.96  0.92  0.02     0.0     0.02    0.0     \n",
      "TP-LinkPlugHS110 0   4   DT      0.99  0.98  0.96  0.98  0.97  0.95  0.02     0.0     0.02    0.0     \n",
      "TP-LinkPlugHS110 0   5   DT      0.98  0.97  0.96  0.97  0.97  0.93  0.03     0.0     0.03    0.0     \n",
      "TP-LinkPlugHS110 0   6   DT      0.99  0.96  0.98  0.96  0.97  0.94  0.03     0.0     0.03    0.0     \n",
      "TP-LinkPlugHS110 0   7   DT      0.98  0.97  0.96  0.97  0.96  0.92  0.02     0.0     0.02    0.0     \n",
      "TP-LinkPlugHS110 0   8   DT      0.98  0.97  0.97  0.97  0.97  0.94  0.02     0.0     0.02    0.0     \n",
      "TP-LinkPlugHS110 0   9   DT      0.98  0.97  0.96  0.97  0.97  0.93  0.02     0.0     0.02    0.0     \n",
      "TP-LinkPlugHS110 0   10  DT      0.98  0.96  0.97  0.96  0.96  0.93  0.02     0.0     0.02    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.990185  0.986705  0.988403  256.000000\n",
      "1              0.936422  0.952219  0.943363   51.200000\n",
      "accuracy       0.980796  0.980796  0.980796    0.980796\n",
      "macro avg      0.963304  0.969462  0.965883  307.200000\n",
      "weighted avg   0.981329  0.980796  0.980870  307.200000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "WeMoInsightSwitch 0   1   DT      0.97  0.95  0.96  0.95  0.95  0.91  0.05     0.0     0.05    0.0     \n",
      "WeMoInsightSwitch 0   2   DT      0.96  0.91  0.95  0.91  0.93  0.86  0.03     0.0     0.03    0.0     \n",
      "WeMoInsightSwitch 0   3   DT      0.96  0.94  0.94  0.94  0.94  0.88  0.05     0.0     0.05    0.0     \n",
      "WeMoInsightSwitch 0   4   DT      0.96  0.92  0.92  0.92  0.92  0.85  0.05     0.0     0.05    0.0     \n",
      "WeMoInsightSwitch 0   5   DT      0.98  0.95  0.95  0.95  0.95  0.89  0.05     0.0     0.05    0.0     \n",
      "WeMoInsightSwitch 0   6   DT      0.97  0.93  0.95  0.93  0.94  0.88  0.05     0.0     0.05    0.0     \n",
      "WeMoInsightSwitch 0   7   DT      0.96  0.91  0.93  0.91  0.92  0.84  0.06     0.0     0.06    0.0     \n",
      "WeMoInsightSwitch 0   8   DT      0.98  0.96  0.96  0.96  0.96  0.92  0.04     0.0     0.04    0.0     \n",
      "WeMoInsightSwitch 0   9   DT      0.97  0.94  0.96  0.94  0.95  0.9   0.06     0.0     0.06    0.0     \n",
      "WeMoInsightSwitch 0   10  DT      0.98  0.94  0.96  0.94  0.95  0.9   0.05     0.0     0.05    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.977910  0.983995  0.980930  500.000000\n",
      "1              0.917615  0.889790  0.903186  100.000000\n",
      "accuracy       0.968167  0.968167  0.968167    0.968167\n",
      "macro avg      0.947763  0.936893  0.942058  600.000000\n",
      "weighted avg   0.967969  0.968167  0.967961  600.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "WeMoLink        0   1   DT      0.97  0.95  0.96  0.95  0.95  0.9   0.08     0.0     0.08    0.0     \n",
      "WeMoLink        0   2   DT      0.97  0.95  0.95  0.95  0.95  0.9   0.06     0.0     0.06    0.0     \n",
      "WeMoLink        0   3   DT      0.96  0.92  0.93  0.92  0.93  0.85  0.06     0.0     0.06    0.0     \n",
      "WeMoLink        0   4   DT      0.96  0.93  0.93  0.93  0.93  0.86  0.06     0.0     0.06    0.0     \n",
      "WeMoLink        0   5   DT      0.96  0.92  0.92  0.92  0.92  0.84  0.06     0.0     0.06    0.0     \n",
      "WeMoLink        0   6   DT      0.98  0.96  0.94  0.96  0.95  0.9   0.08     0.0     0.08    0.0     \n",
      "WeMoLink        0   7   DT      0.96  0.95  0.93  0.95  0.94  0.88  0.06     0.0     0.06    0.0     \n",
      "WeMoLink        0   8   DT      0.96  0.93  0.93  0.93  0.93  0.86  0.06     0.0     0.06    0.0     \n",
      "WeMoLink        0   9   DT      0.96  0.93  0.95  0.93  0.94  0.87  0.07     0.0     0.07    0.0     \n",
      "WeMoLink        0   10  DT      0.96  0.94  0.93  0.94  0.94  0.87  0.06     0.0     0.06    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.979568  0.978189  0.978868  500.000000\n",
      "1              0.892089  0.898319  0.894976  100.000000\n",
      "accuracy       0.964833  0.964833  0.964833    0.964833\n",
      "macro avg      0.935828  0.938254  0.936922  600.000000\n",
      "weighted avg   0.965020  0.964833  0.964878  600.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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gDvs23BeAfRruw8b1G0mVhfDYxe+mHE9UajMFtmpo3qwpJSXpd7yWlJTSvFnTb21zRX5vnnt+1u5ummRYU/IZkx6ewuS5T/Dk25PZtHEzs6a9RG69XI784fcBOK3nqTQ7uBkAf33sKdq0PYQn35rEn14cw303PUD6xUl7lhSeqNRm1e5nm1m+u/+pgnUDgAEAltuInJyG1T1NnbD9P/7TT/t38vP7cNrpF2SpRQKwX6P9OKnrv9O780/Y9Pkmbn7oJs76URdGDrqVghEDqd+gPvNffovyVLpX1un04yla9AHXXzKYlocezJ1PjGJh1/fYsmlLlr/J7hXCqOjO9NhurmiFu4929+Pd/fgQg9onpWto0SL9v3yLFs0o/fSzbet+8IN2PPzQHfzowitYu3ZdtpooQMeTO/DxqhI2rN1AqizFK3+bTfuO7Vn89hJ+fuH1DDznGt6du5BVy9JpafdLuvPK314FYPWHH/HxqhIOOaJ1Nr9CVoTQY6s0sJnZwgrKe0Dz3dTGWueZaS/Q97KLAeh72cVMm5YeOWvd+mCmTBrD5fnXsnTpsmw2UYDSj0o5+rh2NNi7AQAdTj6OFUUryWuSB0D9verTZ9B/MnX8tPT2q0vpeHIHABo3zaP1v7Tm4xUfZ6Xt2VTunqjUZlWlos2BbsD2XQ8DXt8lLaplHh9/P6ed2pmmTQ/gw2XzuXnknYy6434mPvEQ+Zf3YeXKYnpf+lMAbvzV9TRp0pj77vsNAGVlZZzYuUc2m79HW7LgH7z87CuMee5BUmUpli4q4pk//y/9h+TTucsJWE4OUwunseD1dwAovOdxhv3hl4x9cQwGjP7NGDas+zyr3yEbanfISsYquzhqZo8Cf4peVb/9uifc/dKqTlBvr5Yh/DntkU5u1i7bTZCd8FLxi9V6ZGSfNr0S/c5OWPFUrX0kZaU9NnfvX8m6KoOaiNQ9IQwe7Hl3H4pIpWr7PWpJKLCJSExtH/FMQoFNRGJCuClZMw9EJKamplSZ2VgzKzWzv2fUHWBm081safSzcVRvZnavmRVFt5R1yNinX7T9UjPrl+Q7KLCJSEwN3qD7GNB9u7phwAx3bwvMiJYBzgbaRmUA8CCkAyEwAjgB6ASM2BoMK6PAJiIxNdVjc/dXgLXbVZ8PjIs+jwN6ZdQXetocIM/MDiJ9H+10d1/r7uuA6Xw7WH6LApuIxLh7omJmA8xsfkYZkODwzd1963SOEr6ZwdQSWJWxXXFUV1F9pTR4ICIxSUdF3X00MLq653F3N7NdMlKhHpuIxOzi57F9EqWYRD9Lo/rVQOYTB1pFdRXVV0qBTURikqai1TQV2Dqy2Q94OqO+bzQ6eiKwIUpZnwe6mlnjaNCga1RXKaWiIhKTqqFJVWY2ATgdaGpmxaRHN28HJptZf2AFcEm0+bNAD6AI2ALkA7j7WjO7BZgXbTfS3bcfkPgWBTYRiampRxK5e58KVnXZwbYOFFRwnLHA2O9ybgU2EYmp+/MOFNhEZDtlATzfQ4FNRGJCmCuqwCYiMXpskYgEp9yViopIYNRjE5Hg6BqbiASnpm7QzSYFNhGJqe3vDE1CgU1EYlIaPBCR0LgGD0QkNEpFRSQ4SkVFJDhKRUUkOK4em4iERqmoiARHU6pEJDiaUiUiwVEqKiLBUY9NRIKja2wiEpxUuVJREQmMUlERCY5SUREJjlJREQmO5oqKSHD02CIRCY5evyciwdGoqIgEJ4TAZiF8iWwyswHuPjrb7ZDq0d9fmHKy3YAADMh2A2Sn6O8vQApsIhIcBTYRCY4C287T9Zm6TX9/AdLggYgERz02EQmOAttOMLPuZva+mRWZ2bBst0eSM7OxZlZqZn/Pdluk5imwVZOZ5QL3A2cDRwN9zOzo7LZKvoPHgO7ZboTsGgps1dcJKHL3Ze7+FTAROD/LbZKE3P0VYG222yG7hgJb9bUEVmUsF0d1IpJlCmwiEhwFtupbDbTOWG4V1YlIlimwVd88oK2ZHWZmewG9galZbpOIoMBWbe5eBlwDPA8sASa7+6LstkqSMrMJwBvAkWZWbGb9s90mqTmaeSAiwVGPTUSCo8AmIsFRYBOR4CiwiUhwFNhEJDgKbCISHAU2EQmOApuIBOf/AXGl12RUDqKaAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "WeMoSwitch      0   1   DT      0.97  0.95  0.95  0.95  0.95  0.9   0.05     0.0     0.05    0.0     \n",
      "WeMoSwitch      0   2   DT      0.96  0.96  0.93  0.96  0.94  0.88  0.06     0.0     0.06    0.0     \n",
      "WeMoSwitch      0   3   DT      0.96  0.95  0.93  0.95  0.94  0.87  0.06     0.0     0.06    0.0     \n",
      "WeMoSwitch      0   4   DT      0.96  0.94  0.94  0.94  0.94  0.88  0.06     0.0     0.06    0.0     \n",
      "WeMoSwitch      0   5   DT      0.97  0.95  0.95  0.95  0.95  0.91  0.07     0.0     0.07    0.0     \n",
      "WeMoSwitch      0   6   DT      0.97  0.94  0.94  0.94  0.94  0.88  0.05     0.0     0.05    0.0     \n",
      "WeMoSwitch      0   7   DT      0.97  0.95  0.96  0.95  0.95  0.91  0.06     0.0     0.06    0.0     \n",
      "WeMoSwitch      0   8   DT      0.96  0.93  0.91  0.93  0.92  0.84  0.05     0.0     0.05    0.0     \n",
      "WeMoSwitch      0   9   DT      0.96  0.95  0.92  0.95  0.93  0.87  0.05     0.0     0.05    0.0     \n",
      "WeMoSwitch      0   10  DT      0.98  0.97  0.96  0.97  0.96  0.93  0.05     0.0     0.05    0.0     \n",
      "              precision    recall  f1-score  support\n",
      "0              0.983139  0.978419  0.980758  500.000\n",
      "1              0.894581  0.915460  0.904571  100.000\n",
      "accuracy       0.968000  0.968000  0.968000    0.968\n",
      "macro avg      0.938860  0.946939  0.942664  600.000\n",
      "weighted avg   0.968529  0.968000  0.968172  600.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML_alg  Acc   b_Acc Prec  Rec   F1    kap   tra-T    test-T  total   al-time \n",
      "Withings        0   1   DT      1.0   0.99  1.0   0.99  0.99  0.99  0.02     0.0     0.02    0.0     \n",
      "Withings        0   2   DT      1.0   1.0   0.99  1.0   1.0   0.99  0.02     0.0     0.02    0.0     \n",
      "Withings        0   3   DT      0.99  0.99  0.99  0.99  0.99  0.98  0.02     0.0     0.02    0.0     \n",
      "Withings        0   4   DT      1.0   1.0   0.99  1.0   0.99  0.99  0.02     0.0     0.02    0.0     \n",
      "Withings        0   5   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "Withings        0   6   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.0      0.0     0.0     0.0     \n",
      "Withings        0   7   DT      1.0   1.0   0.99  1.0   0.99  0.99  0.02     0.0     0.02    0.0     \n",
      "Withings        0   8   DT      1.0   1.0   1.0   1.0   1.0   1.0   0.01     0.0     0.01    0.0     \n",
      "Withings        0   9   DT      0.99  1.0   0.98  1.0   0.99  0.98  0.02     0.0     0.02    0.0     \n",
      "Withings        0   10  DT      1.0   1.0   1.0   1.0   1.0   1.0   0.02     0.0     0.02    0.0     \n",
      "              precision    recall  f1-score     support\n",
      "0              0.999269  0.997827  0.998545  277.500000\n",
      "1              0.989604  0.996666  0.993075   55.500000\n",
      "accuracy       0.997598  0.997598  0.997598    0.997598\n",
      "macro avg      0.994436  0.997246  0.995810  333.000000\n",
      "weighted avg   0.997635  0.997598  0.997604  333.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in name_list:\n",
    "    step=1\n",
    "    Tree=True # It uses the graphviz library.\n",
    "    output_csv=\"./results/\"+i[18:-4]+\"_\"+str(step)+\"_\"+str(Tree)+\".csv\"\n",
    "    target_names=target_name(i)\n",
    "    ML(i,i,output_csv,features,step,Tree)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### voting process for device csv files\n",
    "#### each voting action is saved in a separate csv file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# IMPORTANT !!!\n",
    "# if you have any error about xverse please see: https://github.com/kahramankostas/IoTDevIDv2/issues/2\n",
    "\n",
    "### and be sure about requirements.txt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xverse.ensemble import VotingSelector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0       IP_flags                  1              1   \n",
      "29      pck_size                  1              1   \n",
      "20   TCP_dataofs                  1              1   \n",
      "44        DNS_id                  1              1   \n",
      "23      Protocol                  1              1   \n",
      "..           ...                ...            ...   \n",
      "62    BOOTP_secs                  0              0   \n",
      "61      ICMP_seq                  0              0   \n",
      "58     ICMP_type                  0              0   \n",
      "55       TCP_SYN                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "29                              1            1           1      1      6  \n",
      "20                              1            1           1      1      6  \n",
      "44                              1            1           1      1      6  \n",
      "23                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "62                              0            0           0      0      0  \n",
      "61                              0            0           0      0      0  \n",
      "58                              0            0           0      0      0  \n",
      "55                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "25   DNS_nscount                  1              1   \n",
      "1          dport                  1              1   \n",
      "31   TCP_dataofs                  1              1   \n",
      "38        IP_tos                  1              1   \n",
      "28         sport                  1              1   \n",
      "..           ...                ...            ...   \n",
      "66    BOOTP_hops                  0              0   \n",
      "67        DNS_ad                  0              0   \n",
      "68        DNS_cd                  0              0   \n",
      "69    DNS_length                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "25                              1            1           1      1      6  \n",
      "1                               1            1           1      1      6  \n",
      "31                              1            1           1      1      6  \n",
      "38                              1            1           1      1      6  \n",
      "28                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "66                              0            0           0      0      0  \n",
      "67                              0            0           0      0      0  \n",
      "68                              0            0           0      0      0  \n",
      "69                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0      UDP_dport                  1              1   \n",
      "16       TCP_seq                  1              1   \n",
      "33      pck_size                  1              1   \n",
      "32   BOOTP_flags                  1              1   \n",
      "31        IP_len                  1              1   \n",
      "..           ...                ...            ...   \n",
      "63     ICMP_code                  0              0   \n",
      "61      ICMP_seq                  0              0   \n",
      "59     ICMP_type                  0              0   \n",
      "57      LLC_ssap                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "16                              1            1           1      1      6  \n",
      "33                              1            1           1      1      6  \n",
      "32                              1            1           1      1      6  \n",
      "31                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "63                              0            0           0      0      0  \n",
      "61                              0            0           0      0      0  \n",
      "59                              0            0           0      0      0  \n",
      "57                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0      TCP_dport                  1              1   \n",
      "13       UDP_len                  1              1   \n",
      "29       TCP_FIN                  1              1   \n",
      "37   DNS_nscount                  1              1   \n",
      "38   DNS_qdcount                  1              1   \n",
      "..           ...                ...            ...   \n",
      "62   DNS_arcount                  0              0   \n",
      "61      ICMP_seq                  0              0   \n",
      "60      LLC_ssap                  0              0   \n",
      "55  DHCP_options                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "13                              1            1           1      1      6  \n",
      "29                              1            1           1      1      6  \n",
      "37                              1            1           1      1      6  \n",
      "38                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "62                              0            0           0      0      0  \n",
      "61                              0            0           0      0      0  \n",
      "60                              0            0           0      0      0  \n",
      "55                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0    dport_class                  1              1   \n",
      "12    Ether_type                  1              1   \n",
      "34   DNS_ancount                  1              1   \n",
      "31    TCP_chksum                  1              1   \n",
      "40        IP_tos                  1              1   \n",
      "..           ...                ...            ...   \n",
      "67    TCP_urgptr                  0              0   \n",
      "66    BOOTP_hops                  0              0   \n",
      "65  TCP_reserved                  0              0   \n",
      "63     ICMP_code                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "12                              1            1           1      1      6  \n",
      "34                              1            1           1      1      6  \n",
      "31                              1            1           1      1      6  \n",
      "40                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "67                              0            0           0      0      0  \n",
      "66                              0            0           0      0      0  \n",
      "65                              0            0           0      0      0  \n",
      "63                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "    Variable_Name  Information_Value  Random_Forest  \\\n",
      "0           sport                  1              1   \n",
      "15        TCP_SYN                  1              1   \n",
      "43      BOOTP_xid                  1              1   \n",
      "37        UDP_len                  1              1   \n",
      "36      UDP_sport                  1              1   \n",
      "..            ...                ...            ...   \n",
      "63      ICMP_code                  0              0   \n",
      "56       LLC_ssap                  0              0   \n",
      "52     IP_options                  0              0   \n",
      "49  EAPOL_version                  0              0   \n",
      "90          IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "15                              1            1           1      1      6  \n",
      "43                              1            1           1      1      6  \n",
      "37                              1            1           1      1      6  \n",
      "36                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "63                              0            0           0      0      0  \n",
      "56                              0            0           0      0      0  \n",
      "52                              0            0           0      0      0  \n",
      "49                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Variable_Name  Information_Value  Random_Forest  \\\n",
      "0           sport                  1              1   \n",
      "15    DNS_ancount                  1              1   \n",
      "1       TCP_sport                  1              1   \n",
      "40  payload_bytes                  1              1   \n",
      "35      BOOTP_xid                  1              1   \n",
      "..            ...                ...            ...   \n",
      "63      ICMP_code                  0              0   \n",
      "61       ICMP_seq                  0              0   \n",
      "56       LLC_ssap                  0              0   \n",
      "54     EAPOL_type                  0              0   \n",
      "90          IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "15                              1            1           1      1      6  \n",
      "1                               1            1           1      1      6  \n",
      "40                              1            1           1      1      6  \n",
      "35                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "63                              0            0           0      0      0  \n",
      "61                              0            0           0      0      0  \n",
      "56                              0            0           0      0      0  \n",
      "54                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "    Variable_Name  Information_Value  Random_Forest  \\\n",
      "0           sport                  1              1   \n",
      "14        entropy                  1              1   \n",
      "40         IP_tos                  1              1   \n",
      "1       TCP_sport                  1              1   \n",
      "27      BOOTP_xid                  1              1   \n",
      "..            ...                ...            ...   \n",
      "63      ICMP_code                  0              0   \n",
      "62  EAPOL_version                  0              0   \n",
      "56     EAPOL_type                  0              0   \n",
      "55       LLC_ssap                  0              0   \n",
      "90          IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "14                              1            1           1      1      6  \n",
      "40                              1            1           1      1      6  \n",
      "1                               1            1           1      1      6  \n",
      "27                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "63                              0            0           0      0      0  \n",
      "62                              0            0           0      0      0  \n",
      "56                              0            0           0      0      0  \n",
      "55                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "    Variable_Name  Information_Value  Random_Forest  \\\n",
      "0           sport                  1              1   \n",
      "17         IP_ttl                  1              1   \n",
      "35         DNS_id                  1              1   \n",
      "32        TCP_ACK                  1              1   \n",
      "31          IP_id                  1              1   \n",
      "..            ...                ...            ...   \n",
      "62     EAPOL_type                  0              0   \n",
      "55  EAPOL_version                  0              0   \n",
      "53     IP_options                  0              0   \n",
      "51       LLC_ssap                  0              0   \n",
      "90          IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "17                              1            1           1      1      6  \n",
      "35                              1            1           1      1      6  \n",
      "32                              1            1           1      1      6  \n",
      "31                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "62                              0            0           0      0      0  \n",
      "55                              0            0           0      0      0  \n",
      "53                              0            0           0      0      0  \n",
      "51                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0      UDP_dport                  1              1   \n",
      "28        DNS_aa                  1              1   \n",
      "25   BOOTP_flags                  1              1   \n",
      "30     EAPOL_len                  1              1   \n",
      "23   DNS_ancount                  1              1   \n",
      "..           ...                ...            ...   \n",
      "65        DNS_ad                  0              0   \n",
      "64    DNS_length                  0              0   \n",
      "63     ICMP_code                  0              0   \n",
      "62   DNS_arcount                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "28                              1            1           1      1      6  \n",
      "25                              1            1           1      1      6  \n",
      "30                              1            1           1      1      6  \n",
      "23                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "65                              0            0           0      0      0  \n",
      "64                              0            0           0      0      0  \n",
      "63                              0            0           0      0      0  \n",
      "62                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0          dport                  1              1   \n",
      "26    TCP_chksum                  1              1   \n",
      "31     BOOTP_xid                  1              1   \n",
      "38        IP_len                  1              1   \n",
      "20   BOOTP_flags                  1              1   \n",
      "..           ...                ...            ...   \n",
      "65    BOOTP_hops                  0              0   \n",
      "64    BOOTP_secs                  0              0   \n",
      "63   DNS_arcount                  0              0   \n",
      "62      ICMP_seq                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "26                              1            1           1      1      6  \n",
      "31                              1            1           1      1      6  \n",
      "38                              1            1           1      1      6  \n",
      "20                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "65                              0            0           0      0      0  \n",
      "64                              0            0           0      0      0  \n",
      "63                              0            0           0      0      0  \n",
      "62                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0        TCP_seq                  1              1   \n",
      "12       UDP_len                  1              1   \n",
      "25      pck_size                  1              1   \n",
      "24       TCP_ack                  1              1   \n",
      "31     BOOTP_xid                  1              1   \n",
      "..           ...                ...            ...   \n",
      "64    BOOTP_secs                  0              0   \n",
      "63      ICMP_seq                  0              0   \n",
      "62   DNS_arcount                  0              0   \n",
      "61     ICMP_code                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "12                              1            1           1      1      6  \n",
      "25                              1            1           1      1      6  \n",
      "24                              1            1           1      1      6  \n",
      "31                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "64                              0            0           0      0      0  \n",
      "63                              0            0           0      0      0  \n",
      "62                              0            0           0      0      0  \n",
      "61                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0       IP_flags                  1              1   \n",
      "14   BOOTP_flags                  1              1   \n",
      "31       TCP_ack                  1              1   \n",
      "32    TCP_chksum                  1              1   \n",
      "33       TCP_seq                  1              1   \n",
      "..           ...                ...            ...   \n",
      "61      ICMP_seq                  0              0   \n",
      "60   DNS_arcount                  0              0   \n",
      "59     ICMP_type                  0              0   \n",
      "56       TCP_RST                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "14                              1            1           1      1      6  \n",
      "31                              1            1           1      1      6  \n",
      "32                              1            1           1      1      6  \n",
      "33                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "61                              0            0           0      0      0  \n",
      "60                              0            0           0      0      0  \n",
      "59                              0            0           0      0      0  \n",
      "56                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0         IP_ttl                  1              1   \n",
      "14     UDP_dport                  1              1   \n",
      "1       IP_flags                  1              1   \n",
      "36     BOOTP_xid                  1              1   \n",
      "26       TCP_ack                  1              1   \n",
      "..           ...                ...            ...   \n",
      "62      LLC_ssap                  0              0   \n",
      "61   DNS_arcount                  0              0   \n",
      "59      ICMP_seq                  0              0   \n",
      "46       TCP_RST                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "14                              1            1           1      1      6  \n",
      "1                               1            1           1      1      6  \n",
      "36                              1            1           1      1      6  \n",
      "26                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "62                              0            0           0      0      0  \n",
      "61                              0            0           0      0      0  \n",
      "59                              0            0           0      0      0  \n",
      "46                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0         IP_ttl                  1              1   \n",
      "30       TCP_FIN                  1              1   \n",
      "21        IP_len                  1              1   \n",
      "22     UDP_sport                  1              1   \n",
      "23       UDP_len                  1              1   \n",
      "..           ...                ...            ...   \n",
      "60   DNS_arcount                  0              0   \n",
      "59      ICMP_seq                  0              0   \n",
      "56     ICMP_type                  0              0   \n",
      "55  DHCP_options                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "30                              1            1           1      1      6  \n",
      "21                              1            1           1      1      6  \n",
      "22                              1            1           1      1      6  \n",
      "23                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "60                              0            0           0      0      0  \n",
      "59                              0            0           0      0      0  \n",
      "56                              0            0           0      0      0  \n",
      "55                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0    TCP_dataofs                  1              1   \n",
      "15      IP_proto                  1              1   \n",
      "1     TCP_window                  1              1   \n",
      "39       TCP_ack                  1              1   \n",
      "32    UDP_chksum                  1              1   \n",
      "..           ...                ...            ...   \n",
      "65        DNS_tc                  0              0   \n",
      "64    BOOTP_secs                  0              0   \n",
      "62      LLC_ssap                  0              0   \n",
      "60      ICMP_seq                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "15                              1            1           1      1      6  \n",
      "1                               1            1           1      1      6  \n",
      "39                              1            1           1      1      6  \n",
      "32                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "65                              0            0           0      0      0  \n",
      "64                              0            0           0      0      0  \n",
      "62                              0            0           0      0      0  \n",
      "60                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0          dport                  1              1   \n",
      "16    TCP_chksum                  1              1   \n",
      "34       entropy                  1              1   \n",
      "36        IP_tos                  1              1   \n",
      "26         IP_id                  1              1   \n",
      "..           ...                ...            ...   \n",
      "63     ICMP_code                  0              0   \n",
      "61      LLC_ssap                  0              0   \n",
      "60   DNS_arcount                  0              0   \n",
      "59      ICMP_seq                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "16                              1            1           1      1      6  \n",
      "34                              1            1           1      1      6  \n",
      "36                              1            1           1      1      6  \n",
      "26                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "63                              0            0           0      0      0  \n",
      "61                              0            0           0      0      0  \n",
      "60                              0            0           0      0      0  \n",
      "59                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0      BOOTP_xid                  1              1   \n",
      "13    EAPOL_type                  1              1   \n",
      "25        IP_tos                  1              1   \n",
      "22         dport                  1              1   \n",
      "21   dport_class                  1              1   \n",
      "..           ...                ...            ...   \n",
      "64       TCP_URG                  0              0   \n",
      "63    TCP_urgptr                  0              0   \n",
      "60      ICMP_seq                  0              0   \n",
      "59     ICMP_type                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "13                              1            1           1      1      6  \n",
      "25                              1            1           1      1      6  \n",
      "22                              1            1           1      1      6  \n",
      "21                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "64                              0            0           0      0      0  \n",
      "63                              0            0           0      0      0  \n",
      "60                              0            0           0      0      0  \n",
      "59                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0         IP_ttl                  1              1   \n",
      "16   dport_class                  1              1   \n",
      "1        UDP_len                  1              1   \n",
      "30     TCP_flags                  1              1   \n",
      "29        DNS_rd                  1              1   \n",
      "..           ...                ...            ...   \n",
      "64     ICMP_code                  0              0   \n",
      "63    BOOTP_secs                  0              0   \n",
      "62   DNS_arcount                  0              0   \n",
      "60      ICMP_seq                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "16                              1            1           1      1      6  \n",
      "1                               1            1           1      1      6  \n",
      "30                              1            1           1      1      6  \n",
      "29                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "64                              0            0           0      0      0  \n",
      "63                              0            0           0      0      0  \n",
      "62                              0            0           0      0      0  \n",
      "60                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0         IP_ttl                  1              1   \n",
      "13     TCP_flags                  1              1   \n",
      "1    dport_class                  1              1   \n",
      "41       TCP_ack                  1              1   \n",
      "36     BOOTP_xid                  1              1   \n",
      "..           ...                ...            ...   \n",
      "63      LLC_ssap                  0              0   \n",
      "61      ICMP_seq                  0              0   \n",
      "60   DNS_arcount                  0              0   \n",
      "58     ICMP_type                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "13                              1            1           1      1      6  \n",
      "1                               1            1           1      1      6  \n",
      "41                              1            1           1      1      6  \n",
      "36                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "63                              0            0           0      0      0  \n",
      "61                              0            0           0      0      0  \n",
      "60                              0            0           0      0      0  \n",
      "58                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "    Variable_Name  Information_Value  Random_Forest  \\\n",
      "0        IP_flags                  1              1   \n",
      "18  EAPOL_version                  1              1   \n",
      "40        TCP_ack                  1              1   \n",
      "34        UDP_len                  1              1   \n",
      "33      UDP_sport                  1              1   \n",
      "..            ...                ...            ...   \n",
      "61        ICMP_id                  0              0   \n",
      "60     DNS_opcode                  0              0   \n",
      "59    DNS_arcount                  0              0   \n",
      "55        TCP_RST                  0              0   \n",
      "90          IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "18                              1            1           1      1      6  \n",
      "40                              1            1           1      1      6  \n",
      "34                              1            1           1      1      6  \n",
      "33                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "61                              0            0           0      0      0  \n",
      "60                              0            0           0      0      0  \n",
      "59                              0            0           0      0      0  \n",
      "55                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0    dport_class                  1              1   \n",
      "31     TCP_flags                  1              1   \n",
      "35      Protocol                  1              1   \n",
      "30        IP_ttl                  1              1   \n",
      "38       UDP_len                  1              1   \n",
      "..           ...                ...            ...   \n",
      "63    BOOTP_secs                  0              0   \n",
      "62      LLC_ssap                  0              0   \n",
      "60   DNS_arcount                  0              0   \n",
      "59      ICMP_seq                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "31                              1            1           1      1      6  \n",
      "35                              1            1           1      1      6  \n",
      "30                              1            1           1      1      6  \n",
      "38                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "63                              0            0           0      0      0  \n",
      "62                              0            0           0      0      0  \n",
      "60                              0            0           0      0      0  \n",
      "59                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0    dport_class                  1              1   \n",
      "14     BOOTP_xid                  1              1   \n",
      "42    UDP_chksum                  1              1   \n",
      "41     UDP_sport                  1              1   \n",
      "40       UDP_len                  1              1   \n",
      "..           ...                ...            ...   \n",
      "63     ICMP_code                  0              0   \n",
      "62      ICMP_seq                  0              0   \n",
      "61   DNS_arcount                  0              0   \n",
      "56      LLC_ssap                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "14                              1            1           1      1      6  \n",
      "42                              1            1           1      1      6  \n",
      "41                              1            1           1      1      6  \n",
      "40                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "63                              0            0           0      0      0  \n",
      "62                              0            0           0      0      0  \n",
      "61                              0            0           0      0      0  \n",
      "56                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "    Variable_Name  Information_Value  Random_Forest  \\\n",
      "0           dport                  1              1   \n",
      "1     sport_class                  1              1   \n",
      "28         DNS_id                  1              1   \n",
      "27        TCP_PSH                  1              1   \n",
      "34        TCP_seq                  1              1   \n",
      "..            ...                ...            ...   \n",
      "63     BOOTP_secs                  0              0   \n",
      "62    DNS_arcount                  0              0   \n",
      "60       LLC_ssap                  0              0   \n",
      "55  EAPOL_version                  0              0   \n",
      "90          IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "1                               1            1           1      1      6  \n",
      "28                              1            1           1      1      6  \n",
      "27                              1            1           1      1      6  \n",
      "34                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "63                              0            0           0      0      0  \n",
      "62                              0            0           0      0      0  \n",
      "60                              0            0           0      0      0  \n",
      "55                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Variable_Name  Information_Value  Random_Forest  \\\n",
      "0       TCP_sport                  1              1   \n",
      "14        UDP_len                  1              1   \n",
      "29       Protocol                  1              1   \n",
      "28         IP_ttl                  1              1   \n",
      "34         IP_tos                  1              1   \n",
      "..            ...                ...            ...   \n",
      "68         DNS_cd                  0              0   \n",
      "69     TCP_urgptr                  0              0   \n",
      "70  ICMP_reserved                  0              0   \n",
      "71     DNS_length                  0              0   \n",
      "90          IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "14                              1            1           1      1      6  \n",
      "29                              1            1           1      1      6  \n",
      "28                              1            1           1      1      6  \n",
      "34                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "68                              0            0           0      0      0  \n",
      "69                              0            0           0      0      0  \n",
      "70                              0            0           0      0      0  \n",
      "71                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "    Variable_Name  Information_Value  Random_Forest  \\\n",
      "0           dport                  1              1   \n",
      "16          IP_DF                  1              1   \n",
      "34         DNS_qr                  1              1   \n",
      "28     TCP_chksum                  1              1   \n",
      "27    TCP_options                  1              1   \n",
      "..            ...                ...            ...   \n",
      "63     BOOTP_secs                  0              0   \n",
      "61       LLC_ssap                  0              0   \n",
      "58     EAPOL_type                  0              0   \n",
      "57  EAPOL_version                  0              0   \n",
      "90          IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "16                              1            1           1      1      6  \n",
      "34                              1            1           1      1      6  \n",
      "28                              1            1           1      1      6  \n",
      "27                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "63                              0            0           0      0      0  \n",
      "61                              0            0           0      0      0  \n",
      "58                              0            0           0      0      0  \n",
      "57                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n",
      "   Variable_Name  Information_Value  Random_Forest  \\\n",
      "0         IP_ttl                  1              1   \n",
      "14      IP_proto                  1              1   \n",
      "38   TCP_options                  1              1   \n",
      "35      Protocol                  1              1   \n",
      "34     BOOTP_xid                  1              1   \n",
      "..           ...                ...            ...   \n",
      "62   DNS_arcount                  0              0   \n",
      "61      ICMP_seq                  0              0   \n",
      "58     ICMP_type                  0              0   \n",
      "55       TCP_FIN                  0              0   \n",
      "90         IP_MF                  0              0   \n",
      "\n",
      "    Recursive_Feature_Elimination  Extra_Trees  Chi_Square  L_One  Votes  \n",
      "0                               1            1           1      1      6  \n",
      "14                              1            1           1      1      6  \n",
      "38                              1            1           1      1      6  \n",
      "35                              1            1           1      1      6  \n",
      "34                              1            1           1      1      6  \n",
      "..                            ...          ...         ...    ...    ...  \n",
      "62                              0            0           0      0      0  \n",
      "61                              0            0           0      0      0  \n",
      "58                              0            0           0      0      0  \n",
      "55                              0            0           0      0      0  \n",
      "90                              0            0           0      0      0  \n",
      "\n",
      "[91 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "for i in name_list:\n",
    "    df=pd.read_csv(i,usecols=features) \n",
    "    X =df[df.columns[0:-1]]\n",
    "    target_names=sorted(list(df[df.columns[-1]].unique()))\n",
    "    y=df[df.columns[-1]]\n",
    "\n",
    "    feature_names=df.columns\n",
    "    \n",
    "    \n",
    "    clf = VotingSelector()\n",
    "    clf.fit(X, y)\n",
    "    #Selected features\n",
    "    temp=\"./results/\"+i[18:-4]+\"_FI_.csv\"\n",
    "    fi=clf.feature_importances_\n",
    "    fi.to_csv(temp, index=None)\n",
    "\n",
    "    temp=\"./results/\"+i[18:-4]+\"_VETO_.csv\"\n",
    "    votes=clf.feature_votes_\n",
    "    votes.to_csv(temp, index=None)\n",
    "    print(clf.feature_votes_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pck_size', 'Ether_type', 'LLC_dsap', 'LLC_ssap', 'LLC_ctrl',\n",
       "       'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_ihl',\n",
       "       'IP_tos', 'IP_len', 'IP_flags', 'IP_Z', 'IP_MF', 'IP_id', 'IP_DF',\n",
       "       'IP_frag', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code',\n",
       "       'ICMP_chksum', 'ICMP_id', 'ICMP_seq', 'ICMP_ts_ori', 'ICMP_ts_rx',\n",
       "       'ICMP_ts_tx', 'ICMP_ptr', 'ICMP_reserved', 'ICMP_length',\n",
       "       'ICMP_nexthopmtu', 'ICMP_unused', 'TCP_seq', 'TCP_ack', 'TCP_dataofs',\n",
       "       'TCP_reserved', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_PSH',\n",
       "       'TCP_ACK', 'TCP_URG', 'TCP_ECE', 'TCP_CWR', 'TCP_window', 'TCP_chksum',\n",
       "       'TCP_urgptr', 'TCP_options', 'UDP_len', 'UDP_chksum', 'DHCP_options',\n",
       "       'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_hops', 'BOOTP_xid',\n",
       "       'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file',\n",
       "       'BOOTP_options', 'DNS_length', 'DNS_id', 'DNS_qr', 'DNS_opcode',\n",
       "       'DNS_aa', 'DNS_tc', 'DNS_rd', 'DNS_ra', 'DNS_z', 'DNS_ad', 'DNS_cd',\n",
       "       'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'DNS_arcount',\n",
       "       'sport_class', 'dport_class', 'sport', 'dport', 'TCP_sport',\n",
       "       'TCP_dport', 'UDP_sport', 'UDP_dport', 'payload_bytes', 'entropy',\n",
       "       'Protocol', 'Label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate the average of the votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./results/Aria_VETO_.csv',\n",
       " './results/D-LinkCam_VETO_.csv',\n",
       " './results/D-LinkDayCam_VETO_.csv',\n",
       " './results/D-LinkDoorSensor_VETO_.csv',\n",
       " './results/D-LinkHomeHub_VETO_.csv',\n",
       " './results/D-LinkSensor_VETO_.csv',\n",
       " './results/D-LinkSiren_VETO_.csv',\n",
       " './results/D-LinkSwitch_VETO_.csv',\n",
       " './results/D-LinkWaterSensor_VETO_.csv',\n",
       " './results/EdimaxCam_VETO_.csv',\n",
       " './results/EdimaxPlug1101W_VETO_.csv',\n",
       " './results/EdimaxPlug2101W_VETO_.csv',\n",
       " './results/EdnetCam_VETO_.csv',\n",
       " './results/EdnetGateway_VETO_.csv',\n",
       " './results/HomeMaticPlug_VETO_.csv',\n",
       " './results/HueBridge_VETO_.csv',\n",
       " './results/HueSwitch_VETO_.csv',\n",
       " './results/IKettle2_VETO_.csv',\n",
       " './results/Lightify_VETO_.csv',\n",
       " './results/MAXGateway_VETO_.csv',\n",
       " './results/SmarterCoffee_VETO_.csv',\n",
       " './results/TP-LinkPlugHS100_VETO_.csv',\n",
       " './results/TP-LinkPlugHS110_VETO_.csv',\n",
       " './results/WeMoInsightSwitch_VETO_.csv',\n",
       " './results/WeMoLink_VETO_.csv',\n",
       " './results/WeMoSwitch_VETO_.csv',\n",
       " './results/Withings_VETO_.csv']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_list=find_the_way('./results/','_VETO_.csv')\n",
    "name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_add = pd.DataFrame(columns=[ 'Information_Value', 'Random_Forest',\n",
    "       'Recursive_Feature_Elimination', 'Extra_Trees', 'Chi_Square', 'L_One',\n",
    "       'Votes'])\n",
    "\n",
    "flag=1\n",
    "for i in name_list:\n",
    "    df = pd.read_csv(i, index_col=0)\n",
    "    df=df.sort_index()    \n",
    "    df_add= df_add.add(df, fill_value=0)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df_add/27\n",
    "df=df.sort_values(['Votes'], ascending=[False])\n",
    "df.to_csv(\"veto_average_results.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Information_Value</th>\n",
       "      <th>Random_Forest</th>\n",
       "      <th>Recursive_Feature_Elimination</th>\n",
       "      <th>Extra_Trees</th>\n",
       "      <th>Chi_Square</th>\n",
       "      <th>L_One</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCP_sport</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.96296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sport</th>\n",
       "      <td>0.962963</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>1</td>\n",
       "      <td>5.92593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCP_dport</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.92593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dport</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1</td>\n",
       "      <td>5.88889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UDP_sport</th>\n",
       "      <td>0.925926</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>1</td>\n",
       "      <td>5.88889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICMP_ts_ori</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICMP_ts_rx</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICMP_ts_tx</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICMP_unused</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_MF</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Information_Value Random_Forest Recursive_Feature_Elimination  \\\n",
       "Variable_Name                                                                 \n",
       "TCP_sport                     1             1                             1   \n",
       "sport                  0.962963             1                             1   \n",
       "TCP_dport                     1             1                             1   \n",
       "dport                         1             1                             1   \n",
       "UDP_sport              0.925926             1                             1   \n",
       "...                         ...           ...                           ...   \n",
       "ICMP_ts_ori                   0             0                             0   \n",
       "ICMP_ts_rx                    0             0                             0   \n",
       "ICMP_ts_tx                    0             0                             0   \n",
       "ICMP_unused                   0             0                             0   \n",
       "IP_MF                         0             0                             0   \n",
       "\n",
       "              Extra_Trees Chi_Square L_One    Votes  \n",
       "Variable_Name                                        \n",
       "TCP_sport        0.962963          1     1  5.96296  \n",
       "sport                   1   0.962963     1  5.92593  \n",
       "TCP_dport        0.925926          1     1  5.92593  \n",
       "dport                   1   0.888889     1  5.88889  \n",
       "UDP_sport               1   0.962963     1  5.88889  \n",
       "...                   ...        ...   ...      ...  \n",
       "ICMP_ts_ori             0          0     0        0  \n",
       "ICMP_ts_rx              0          0     0        0  \n",
       "ICMP_ts_tx              0          0     0        0  \n",
       "ICMP_unused             0          0     0        0  \n",
       "IP_MF                   0          0     0        0  \n",
       "\n",
       "[91 rows x 7 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the voting process result graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "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>Variable_Name</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCP_sport</td>\n",
       "      <td>5.962963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sport</td>\n",
       "      <td>5.925926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCP_dport</td>\n",
       "      <td>5.925926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dport</td>\n",
       "      <td>5.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UDP_sport</td>\n",
       "      <td>5.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>ICMP_ts_ori</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>ICMP_ts_rx</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>ICMP_ts_tx</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>ICMP_unused</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>IP_MF</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Variable_Name     Votes\n",
       "0      TCP_sport  5.962963\n",
       "1          sport  5.925926\n",
       "2      TCP_dport  5.925926\n",
       "3          dport  5.888889\n",
       "4      UDP_sport  5.888889\n",
       "..           ...       ...\n",
       "86   ICMP_ts_ori  0.000000\n",
       "87    ICMP_ts_rx  0.000000\n",
       "88    ICMP_ts_tx  0.000000\n",
       "89   ICMP_unused  0.000000\n",
       "90         IP_MF  0.000000\n",
       "\n",
       "[91 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"veto_average_results.csv\")\n",
    "new = data[['Variable_Name', 'Votes']].copy()\n",
    "\n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_name=\"Feature Selection with Voting.PDF\"\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "import matplotlib.pylab as pylab\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (10, 5),\n",
    "         'axes.labelsize': 'x-large',\n",
    "         'axes.titlesize':'x-large',\n",
    "         'xtick.labelsize':'medium',\n",
    "         'ytick.labelsize':'x-large'}\n",
    "pylab.rcParams.update(params)\n",
    "plt.figure(figsize=(18,8))\n",
    "plt.title(\"Feature Selection with Voting\")\n",
    "\n",
    "plt.ylabel('Voting Average')\n",
    "plt.xticks(rotation=90) \n",
    "ax = sns.barplot(x=\"Variable_Name\",color='b', y=\"Votes\", data=new)\n",
    "plt.xlabel('Feature Name')\n",
    "plt.savefig(graph_name,bbox_inches='tight',format=\"pdf\")#, dpi=400)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"veto_average_results.csv\",index_col=\"Variable_Name\")\n",
    "del data[\"Votes\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_name=\"Feature Selection with Voting2.PDF\"\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (10, 5),\n",
    "         'axes.labelsize': 'x-large',\n",
    "         'axes.titlesize':'x-large',\n",
    "         'xtick.labelsize':'medium',\n",
    "         'ytick.labelsize':'x-large'}\n",
    "plt.rcParams.update(params)\n",
    "import matplotlib.pylab as pylab\n",
    "\n",
    "#pylab.rcParams.update(params)\n",
    "\n",
    "data.plot.bar(stacked=True,figsize=(18,8))\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Votes')\n",
    "plt.savefig(graph_name,bbox_inches='tight',format=\"pdf\")#, dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_name=\"Feature Selection with Voting3.PDF\"\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (10, 5),\n",
    "         'axes.labelsize': 'x-large',\n",
    "         'axes.titlesize':'x-large',\n",
    "         'xtick.labelsize':'medium',\n",
    "         'ytick.labelsize':'x-large'}\n",
    "plt.rcParams.update(params)\n",
    "import matplotlib.pylab as pylab\n",
    "\n",
    "#pylab.rcParams.update(params)\n",
    "\n",
    "data.plot.bar(stacked=True,figsize=(18,8))\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Votes')\n",
    "plt.savefig(graph_name,bbox_inches='tight',format=\"pdf\")#, dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}