0002 Feature Selection/02.3 Feature selection process using genetic algorithm Aalto.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# genetic algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import os\n",
    "import seaborn as sns\n",
    "%matplotlib inline \n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import  f1_score\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import statistics as stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=feature=[#'IP_id',\n",
    "#'ICMP_chksum',\n",
    "#'ICMP_id',\n",
    "#'TCP_seq',\n",
    "#'TCP_ack',\n",
    " #'TCP_chksum',\n",
    "# 'UDP_chksum',\n",
    "# 'DNS_id',\n",
    "# 'BOOTP_xid',\n",
    "'sport_class',\n",
    "'dport_class',\n",
    "#'sport23',\n",
    "#'dport23',\n",
    "#'sport_bare',\n",
    "# 'dport_bare',\n",
    "# 'TCP_sport',\n",
    "# 'TCP_dport',\n",
    "# 'UDP_sport',\n",
    "# 'UDP_dport',\n",
    " 'pck_size',\n",
    " 'Ether_type',\n",
    " 'LLC_ssap',\n",
    " 'LLC_ctrl',\n",
    " 'EAPOL_version',\n",
    " 'EAPOL_type',\n",
    " 'EAPOL_len',\n",
    " 'IP_version',\n",
    " 'IP_ihl',\n",
    " 'IP_tos',\n",
    " 'IP_len',\n",
    " 'IP_flags',\n",
    " 'IP_DF',\n",
    " 'IP_ttl',\n",
    " 'IP_proto',\n",
    " 'IP_options',\n",
    " 'ICMP_type',\n",
    " 'ICMP_code',\n",
    " 'ICMP_seq',\n",
    " 'TCP_dataofs',\n",
    " 'TCP_flags',\n",
    " 'TCP_FIN',\n",
    " 'TCP_SYN',\n",
    " 'TCP_RST',\n",
    " 'TCP_PSH',\n",
    " 'TCP_ACK',\n",
    " 'TCP_window',\n",
    " 'TCP_options',\n",
    " 'UDP_len',\n",
    " 'DHCP_options',\n",
    " 'BOOTP_op',\n",
    " 'BOOTP_htype',\n",
    " 'BOOTP_hlen',\n",
    " 'BOOTP_secs',\n",
    " 'BOOTP_flags',\n",
    " 'BOOTP_sname',\n",
    " 'BOOTP_file',\n",
    " 'BOOTP_options',\n",
    " 'DNS_qr',\n",
    " 'DNS_aa',\n",
    " 'DNS_rd',\n",
    " 'DNS_ra',\n",
    " 'DNS_rcode',\n",
    " 'DNS_qdcount',\n",
    " 'DNS_ancount',\n",
    " 'DNS_nscount',\n",
    " 'DNS_arcount',\n",
    " 'payload_bytes',    \n",
    " 'entropy',\n",
    " 'Protocol',\n",
    "#\"MAC\",\n",
    " 'Label']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(83894, 52) (21182, 52) (83894,) (21182,)\n"
     ]
    }
   ],
   "source": [
    "test='./Aalto_validation_IoTDevID.csv'\n",
    "train='./Aalto_train_IoTDevID.csv'\n",
    "\n",
    "df = pd.read_csv(train,usecols=cols)#,header=None )\n",
    "\n",
    "X_train =df[df.columns[0:-1]]\n",
    "#X_train=np.array(X_train)\n",
    "df[df.columns[-1]] = df[df.columns[-1]].astype('category')\n",
    "y_train=df[df.columns[-1]].cat.codes  \n",
    "\n",
    "\n",
    "\n",
    "df = pd.read_csv(test,usecols=cols)#,header=None )\n",
    "\n",
    "X_test =df[df.columns[0:-1]]\n",
    "#X_test=np.array(X_test)\n",
    "df[df.columns[-1]] = df[df.columns[-1]].astype('category')\n",
    "y_test=df[df.columns[-1]].cat.codes  \n",
    "\n",
    "\n",
    "print(X_train.shape,\n",
    "X_test.shape,\n",
    "y_train.shape,\n",
    "y_test.shape ,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MEAN                           STD                           \n",
      "0.7003946487780672             0.0007677744020127337         \n"
     ]
    }
   ],
   "source": [
    "#training a logistics regression model\n",
    "logmodel = DecisionTreeClassifier()\n",
    "results=[]\n",
    "\n",
    "#print(\"Accuracy = \"+ str(accuracy_score(y_test,predictions)))\n",
    "for i in range(100):\n",
    "    logmodel.fit(X_train,y_train)\n",
    "    predictions = logmodel.predict(X_test)\n",
    "    results.append(f1_score(y_test,predictions,average= \"macro\"))\n",
    "print ('%-30s %-30s' % (\"MEAN\",\"STD\"))\n",
    "print ('%-30s %-30s' % (np.mean(results),np.std(results)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining various steps required for the genetic algorithm\n",
    "# GA adapted from https://datascienceplus.com/genetic-algorithm-in-machine-learning-using-python/\n",
    "def initilization_of_population(size,n_feat):\n",
    "    population = []\n",
    "    for i in range(size):\n",
    "        chromosome = np.ones(n_feat,dtype=np.bool)\n",
    "        chromosome[:int(0.3*n_feat)]=False\n",
    "        np.random.shuffle(chromosome)\n",
    "        population.append(chromosome)\n",
    "    return population\n",
    "\n",
    "def fitness_score(population):\n",
    "    scores = []\n",
    "    for chromosome in population:\n",
    "        logmodel.fit(X_train.iloc[:,chromosome],y_train)\n",
    "        predictions = logmodel.predict(X_test.iloc[:,chromosome])\n",
    "        scores.append(f1_score(y_test,predictions,average= \"macro\"))\n",
    "    scores, population = np.array(scores), np.array(population) \n",
    "    inds = np.argsort(scores)\n",
    "    return list(scores[inds][::-1]), list(population[inds,:][::-1])\n",
    "\n",
    "def selection(pop_after_fit,n_parents):\n",
    "    population_nextgen = []\n",
    "    for i in range(n_parents):\n",
    "        population_nextgen.append(pop_after_fit[i])\n",
    "    return population_nextgen\n",
    "\n",
    "def crossover(pop_after_sel):\n",
    "    population_nextgen=pop_after_sel\n",
    "    for i in range(len(pop_after_sel)):\n",
    "        child=pop_after_sel[i]\n",
    "        child[3:7]=pop_after_sel[(i+1)%len(pop_after_sel)][3:7]\n",
    "        population_nextgen.append(child)\n",
    "    return population_nextgen\n",
    "\n",
    "def mutation(pop_after_cross,mutation_rate):\n",
    "    population_nextgen = []\n",
    "    for i in range(0,len(pop_after_cross)):\n",
    "        chromosome = pop_after_cross[i]\n",
    "        for j in range(len(chromosome)):\n",
    "            if random.random() < mutation_rate:\n",
    "                chromosome[j]= not chromosome[j]\n",
    "        population_nextgen.append(chromosome)\n",
    "    #print(population_nextgen)\n",
    "    return population_nextgen\n",
    "\n",
    "def generations(size,n_feat,n_parents,mutation_rate,n_gen,X_train,\n",
    "                                   X_test, y_train, y_test):\n",
    "    \n",
    "    best_chromo= []\n",
    "    best_score= []\n",
    "    population_nextgen=initilization_of_population(size,n_feat)\n",
    "    for i in range(n_gen):\n",
    "        second=time.time()\n",
    "        scores, pop_after_fit = fitness_score(population_nextgen)\n",
    "        #print(scores[:2])\n",
    "        zaman=time.time()-second\n",
    "        print ('%-30s %-30s %-30s' % (np.mean(scores),np.std(scores),zaman))\n",
    "\n",
    "        pop_after_sel = selection(pop_after_fit,n_parents)\n",
    "        pop_after_cross = crossover(pop_after_sel)\n",
    "        population_nextgen = mutation(pop_after_cross,mutation_rate)\n",
    "        best_chromo.append(pop_after_fit[0])\n",
    "        best_score.append(scores[0])\n",
    "    return best_chromo,best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MEAN                           STD                            TIME                          \n",
      "0.6589763227659767             0.0467366271654974             72.50790762901306             \n",
      "0.6903423841259091             0.016978390471161325           92.58118104934692             \n",
      "0.7037734697205251             0.012562650137698778           92.41869306564331             \n",
      "0.7105832007412997             0.01039437973573768            81.27631998062134             \n",
      "0.7151130877930377             0.011284655381154373           83.58070492744446             \n",
      "0.7163302023674766             0.00934232260986502            82.44272685050964             \n",
      "0.7158258343516155             0.014617278538779392           83.65655159950256             \n",
      "0.7171012067704591             0.011017212047898756           80.1672351360321              \n",
      "0.7147308453850421             0.019049716987980664           80.54223108291626             \n",
      "0.7171476267661043             0.016029285062235776           82.71649694442749             \n",
      "0.7198277629849924             0.0057735169731855255          76.52506685256958             \n",
      "0.7168734497448059             0.018510897898756828           75.77822804450989             \n",
      "0.7195556572612544             0.010543440180874528           77.10858345031738             \n",
      "0.7178455098863457             0.016349384912053278           78.34327983856201             \n",
      "0.7205111757452408             0.005818097669701385           79.08234477043152             \n",
      "0.7176198904725136             0.015205767694067828           82.70643162727356             \n",
      "0.7208947998486515             0.0037717638390406477          81.23180532455444             \n",
      "0.7199152505486759             0.009941688181951827           81.87285161018372             \n",
      "0.7190714188667421             0.008528135637623972           88.76167964935303             \n",
      "0.7177139462189343             0.016691133057710944           83.13189601898193             \n",
      "0.718526890370484              0.01289293388625454            86.99776530265808             \n",
      "0.7194816315195094             0.01072218868313857            81.67547416687012             \n",
      "0.7200866076645197             0.0069463581024509285          80.81278324127197             \n",
      "0.7206592531007951             0.004546578222193825           80.61431384086609             \n",
      "0.7179963410556683             0.012984816289468676           81.79515480995178             \n",
      "0.7206956887404841             0.005341896083036093           81.06012105941772             \n",
      "0.7194232809834188             0.01144008895801389            80.34303951263428             \n",
      "0.7192090177215066             0.011562689655657512           80.60733199119568             \n",
      "0.7201991027555238             0.006902746004850804           80.86433815956116             \n",
      "0.7196679453167495             0.011000771128808012           81.04068446159363             \n",
      "0.7191875107295199             0.016150886087082914           81.08456826210022             \n",
      "0.720041875381283              0.010060680796405808           82.63241600990295             \n",
      "0.7185399195851528             0.014562395343567463           82.23163437843323             \n",
      "0.7203029487112744             0.00938057101305912            82.21468734741211             \n",
      "0.7187840643839932             0.01391932676373365            83.43741083145142             \n",
      "0.7200598808844595             0.0072651492538378225          83.38757467269897             \n",
      "0.720123042713066              0.00979959594310082            84.39095568656921             \n",
      "0.7179333078494686             0.014621541317513103           82.91091895103455             \n",
      "0.7194746791743895             0.010381105526643229           82.99768686294556             \n",
      "0.7208195118992385             0.0031022694810520224          83.12141060829163             \n",
      "0.7204530785173026             0.006517120675609325           83.11345267295837             \n",
      "0.7199961068591586             0.010516190730664676           82.25774359703064             \n",
      "0.7199664143546403             0.010320146235327704           82.05529165267944             \n",
      "0.7176007931224444             0.015379350529328557           82.41138982772827             \n",
      "0.7196347800953732             0.008961604482191427           82.96191453933716             \n",
      "0.7200710054285431             0.009949605607338137           82.90606617927551             \n",
      "0.7199871934030975             0.007959382553614234           81.38216042518616             \n",
      "0.7201154932415348             0.0068570400514424545          82.0763213634491              \n",
      "0.7196960260846289             0.01066744056465618            81.75418376922607             \n",
      "0.721087154318571              0.0025709427291596373          82.81833243370056             \n",
      "0.717968351082655              0.01688230172435582            82.15911626815796             \n",
      "0.719526982925174              0.012959206924149798           82.14017343521118             \n",
      "0.7205212271059679             0.006130189213493501           82.34961533546448             \n",
      "0.7196829234493898             0.010864699947607664           82.13176608085632             \n",
      "0.7181610136081784             0.014943839028365739           82.01696109771729             \n",
      "0.7196158398642967             0.009173977623335989           81.45845699310303             \n",
      "0.7209725532790096             0.004201277976100846           83.03226327896118             \n",
      "0.718621790233365              0.015756869588752623           81.50252318382263             \n",
      "0.7182582329892715             0.016108646233442367           82.29838418960571             \n",
      "0.7187305541144143             0.012144794234430658           84.42069792747498             \n",
      "0.7195144120457057             0.010779636163327234           83.55408716201782             \n",
      "0.720331835551183              0.00579418211299668            82.20675730705261             \n",
      "0.7206064971591911             0.004556958010427162           83.68678998947144             \n",
      "0.7204773541376679             0.006503339344513609           83.24098515510559             \n",
      "0.7200106264930619             0.006944716593230962           82.59978795051575             \n",
      "0.71776338767078               0.016732062451039822           83.20019268989563             \n",
      "0.7189679232358948             0.011837006561158362           83.04263353347778             \n",
      "0.7185102934255319             0.014548744265660427           82.06624627113342             \n",
      "0.7201494176460933             0.00981107873256628            83.14535784721375             \n",
      "0.7196629580506251             0.008359956722880858           82.42532014846802             \n",
      "0.7190151273151268             0.010998077772126132           82.6148293018341              \n",
      "0.7198498980092135             0.007683119696537242           83.35983228683472             \n",
      "0.7180188710801941             0.016532791873810078           82.78337979316711             \n",
      "0.7211562336700285             0.002533160999600684           82.26979470252991             \n",
      "0.7199272795116687             0.007425686701965902           82.7505054473877              \n",
      "0.7190433959896486             0.01263827630079146            82.24685525894165             \n",
      "0.7203373454586761             0.005886640569780072           83.3289704322815              \n",
      "0.71965551840786               0.010730017487757348           82.99586367607117             \n",
      "0.7191873580346224             0.013846053790522733           83.89193940162659             \n",
      "0.7206632336416325             0.005603956039078253           82.91456055641174             \n",
      "0.7201011904698745             0.0070539456709740956          82.65525889396667             \n",
      "0.7206245213107672             0.005406540307655012           82.82387828826904             \n",
      "0.720547255039237              0.009227249697964334           83.21094369888306             \n",
      "0.7188801465859506             0.013897418019186853           81.68802213668823             \n",
      "0.7188247105395581             0.01188604805521808            82.87584066390991             \n",
      "0.719273899585873              0.011263809412310309           82.20772933959961             \n",
      "0.7199886004891927             0.0099911471732281             82.64159035682678             \n",
      "0.719052035976393              0.011603697109475907           82.21972155570984             \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.720159135049198              0.006159985321391933           81.93549752235413             \n",
      "0.7178453371019254             0.016685563926721547           82.1031243801117              \n",
      "0.7200585927404771             0.007018993336421349           82.51801180839539             \n",
      "0.7209126406396161             0.004315895123713204           81.6044590473175              \n",
      "0.7195324798059186             0.010547422597837572           82.67063403129578             \n",
      "0.7200382115479268             0.007469305010173104           82.17299580574036             \n",
      "0.7203845894772577             0.009102790438128831           83.07358241081238             \n",
      "0.7209306296831303             0.00419504130794055            83.71685886383057             \n",
      "0.7190722448147076             0.013491507915830085           87.70822548866272             \n",
      "0.7206276278989598             0.005709347004996504           88.8571527004242              \n",
      "0.7183922899461342             0.015985182457113604           86.20724439620972             \n",
      "0.7183279748438846             0.014288499692389545           77.60229063034058             \n",
      "F1 Score score after genetic algorithm is= 0.721571061121188\n"
     ]
    }
   ],
   "source": [
    "print ('%-30s %-30s %-30s' % (\"MEAN\",\"STD\",\"TIME\"))\n",
    "\n",
    "\n",
    "chromo,score=generations(size=200,n_feat=52,n_parents=120,mutation_rate=0.005,\n",
    "                     n_gen=100,X_train=X_train,X_test=X_test,y_train=y_train,y_test=y_test)\n",
    "logmodel.fit(X_train.iloc[:,chromo[-1]],y_train)\n",
    "predictions = logmodel.predict(X_test.iloc[:,chromo[-1]])\n",
    "print(\"F1 Score score after genetic algorithm is= \"+str(f1_score(y_test,predictions,average= \"macro\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MEAN                           STD                           \n",
      "0.7215949485256491             0.0001474021994194392         \n"
     ]
    }
   ],
   "source": [
    "results=[]\n",
    "for i in range(10):\n",
    "    logmodel.fit(X_train.iloc[:,chromo[-1]],y_train)\n",
    "    predictions = logmodel.predict(X_test.iloc[:,chromo[-1]])\n",
    "    results.append(f1_score(y_test,predictions,average= \"macro\"))\n",
    "print ('%-30s %-30s' % (\"MEAN\",\"STD\"))\n",
    "print ('%-30s %-30s' % (np.mean(results),np.std(results)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        False,  True, False,  True,  True,  True, False]),\n",
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       "         True, False, False,  True,  True,  True,  True,  True,  True,\n",
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       "         True, False, False,  True,  True,  True, False]),\n",
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       "         True, False,  True,  True,  True,  True,  True,  True, False,\n",
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       "        False,  True, False, False,  True,  True, False,  True,  True,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
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       "        False,  True,  True,  True, False, False,  True,  True, False,\n",
       "        False,  True, False,  True,  True,  True, False]),\n",
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       "        False,  True,  True,  True, False, False,  True,  True, False,\n",
       "        False,  True, False,  True,  True,  True, False]),\n",
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       "         True,  True,  True, False,  True, False,  True, False, False,\n",
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       "         True,  True, False,  True,  True,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
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       "         True,  True,  True,  True,  True, False, False, False,  True,\n",
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       "         True,  True,  True,  True,  True,  True,  True,  True, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
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       "         True,  True,  True,  True,  True, False,  True, False,  True,\n",
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       "        False,  True, False,  True,  True,  True, False]),\n",
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       "         True,  True,  True,  True,  True,  True,  True,  True, False,\n",
       "        False,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True, False,  True,  True,  True, False, False,\n",
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       "         True, False,  True,  True,  True,  True, False,  True, False,\n",
       "        False,  True, False,  True,  True,  True, False]),\n",
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       "         True,  True,  True,  True, False, False, False,  True,  True,\n",
       "        False,  True, False,  True,  True,  True, False]),\n",
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       "         True,  True,  True,  True,  True, False,  True, False, False,\n",
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       "         True,  True, False, False,  True,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True,  True,  True,  True, False,\n",
       "         True,  True,  True,  True,  True, False,  True, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False,  True,  True,  True,  True,\n",
       "         True, False, False,  True,  True,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True,  True,  True,  True, False,\n",
       "         True,  True, False, False,  True, False,  True, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False,  True,  True, False,  True,\n",
       "         True, False, False,  True,  True,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True, False,  True,  True, False, False, False,\n",
       "         True,  True,  True,  True,  True, False,  True, False,  True,\n",
       "        False,  True, False,  True, False, False, False, False,  True,\n",
       "        False, False,  True, False,  True,  True,  True, False, False,\n",
       "         True,  True,  True,  True, False, False, False,  True,  True,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True,  True,  True,  True, False,\n",
       "         True,  True,  True, False,  True,  True,  True, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False, False, False,  True,  True,\n",
       "         True,  True, False,  True,  True,  True, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False,  True,  True, False,\n",
       "         True,  True,  True,  True,  True, False,  True, False, False,\n",
       "         True,  True, False,  True, False, False, False, False,  True,\n",
       "         True,  True,  True, False,  True,  True,  True,  True,  True,\n",
       "        False,  True, False,  True, False,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False,  True,  True, False,\n",
       "         True,  True,  True, False,  True,  True,  True, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False,  True, False,  True,  True,\n",
       "         True,  True, False,  True,  True,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True, False,  True,  True, False,\n",
       "         True,  True, False, False,  True, False,  True, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False,  True,  True,  True,  True,\n",
       "         True, False, False, False,  True,  True,  True,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True, False, False,  True,  True, False,  True,  True, False,\n",
       "         True,  True,  True, False,  True, False,  True, False, False,\n",
       "         True,  True, False,  True, False, False, False,  True,  True,\n",
       "         True,  True,  True, False,  True,  True,  True,  True,  True,\n",
       "         True,  True, False,  True,  True,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True,  True,  True,  True, False,\n",
       "         True,  True, False, False,  True, False,  True,  True, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False,  True,  True,  True,  True,\n",
       "         True, False, False,  True,  True,  True, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False,  True,  True, False,\n",
       "         True,  True, False, False,  True,  True,  True, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False,  True,  True,  True,  True,\n",
       "         True, False, False, False,  True,  True,  True,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False, False, False,  True,\n",
       "         True,  True,  True, False, False, False,  True, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True, False,  True,  True,\n",
       "         True,  True, False,  True,  True,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True,  True,  True, False,  True,\n",
       "         True,  True,  True, False,  True, False,  True, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True, False,  True,  True,\n",
       "         True,  True, False,  True,  True,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False,  True,  True, False,\n",
       "         True,  True,  True, False,  True,  True,  True, False,  True,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False, False, False,  True,  True,\n",
       "         True,  True, False,  True,  True, False, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True,  True,  True,  True, False,\n",
       "         True,  True, False, False,  True, False, False, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True,  True,  True,  True,\n",
       "         True, False, False,  True,  True,  True, False, False, False,\n",
       "         True,  True, False,  True,  True, False, False]),\n",
       " array([ True,  True, False,  True,  True,  True, False,  True, False,\n",
       "         True,  True,  True, False,  True,  True,  True, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False,  True,  True,  True,  True, False,\n",
       "         True, False, False, False,  True,  True,  True,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False,  True,  True, False,\n",
       "         True,  True,  True, False,  True, False,  True, False, False,\n",
       "         True,  True, False,  True, False, False, False, False,  True,\n",
       "         True,  True,  True, False,  True,  True, False,  True,  True,\n",
       "         True,  True, False,  True,  True,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True,  True,  True,  True,  True,  True, False,\n",
       "         True,  True,  True,  True,  True,  True,  True, False, False,\n",
       "        False,  True, False,  True, False, False, False, False,  True,\n",
       "        False,  True,  True, False,  True, False,  True,  True,  True,\n",
       "         True,  True,  True,  True, False,  True,  True,  True,  True,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True, False,  True,  True,  True,  True, False,\n",
       "         True,  True,  True,  True,  True,  True,  True, False, False,\n",
       "        False,  True, False,  True, False, False, False, False,  True,\n",
       "        False,  True,  True, False,  True, False,  True,  True,  True,\n",
       "         True,  True,  True,  True, False,  True,  True,  True,  True,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True, False, False, False, False,\n",
       "         True,  True, False, False,  True, False,  True,  True, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False, False,  True, False,  True,\n",
       "         True, False,  True, False, False, False, False, False, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False, False,  True, False,\n",
       "         True,  True,  True, False,  True, False, False, False, False,\n",
       "         True,  True, False,  True,  True, False,  True,  True,  True,\n",
       "         True,  True,  True, False, False,  True,  True,  True,  True,\n",
       "         True, False, False,  True,  True,  True, False, False, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True,  True, False,  True, False,\n",
       "         True,  True,  True, False,  True,  True,  True, False,  True,\n",
       "         True,  True, False,  True, False, False, False, False,  True,\n",
       "         True,  True,  True, False, False,  True,  True,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False,  True,  True, False,\n",
       "         True,  True,  True, False,  True, False, False, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False,  True,  True, False,  True,\n",
       "         True, False, False,  True,  True,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True,  True,  True,  True, False,\n",
       "         True,  True,  True, False,  True, False,  True, False, False,\n",
       "         True,  True, False, False,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False,  True,  True,  True,  True,\n",
       "         True, False, False,  True,  True,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True,  True,  True,  True, False,\n",
       "         True,  True, False, False,  True, False,  True,  True, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False, False, False,  True,  True,\n",
       "        False,  True, False,  True,  True, False, False, False, False,\n",
       "         True, False, False, False,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True, False,  True, False, False,\n",
       "         True,  True, False, False,  True, False,  True,  True, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False, False,  True, False,  True,\n",
       "         True, False,  True, False, False, False, False, False, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True, False,  True, False,  True, False, False,\n",
       "         True,  True,  True,  True,  True, False,  True, False, False,\n",
       "        False,  True, False,  True, False, False, False, False,  True,\n",
       "        False,  True,  True,  True, False,  True,  True,  True,  True,\n",
       "         True, False,  True,  True, False,  True, False, False, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True, False, False,  True,  True,  True,  True,  True, False,\n",
       "         True,  True,  True, False,  True, False, False, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True,  True,  True,  True,\n",
       "         True, False, False,  True, False,  True, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False,  True,  True, False,\n",
       "         True, False, False, False,  True,  True,  True, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False, False, False,  True,  True,\n",
       "         True, False, False, False,  True,  True,  True,  True, False,\n",
       "         True, False, False,  True,  True, False, False]),\n",
       " array([ True, False, False,  True,  True, False, False, False, False,\n",
       "         True,  True,  True, False,  True,  True,  True, False, False,\n",
       "         True,  True, False,  True, False, False, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True,  True,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False, False,  True, False,\n",
       "         True,  True, False,  True,  True, False, False, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True,  True,  True,  True,\n",
       "         True, False, False,  True, False,  True, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True, False,  True,  True,  True,  True, False,\n",
       "         True,  True,  True, False,  True, False,  True, False, False,\n",
       "         True,  True, False,  True,  True,  True, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True,  True,  True,  True,\n",
       "         True, False, False,  True,  True,  True, False,  True,  True,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True,  True,  True, False,  True,  True, False,\n",
       "         True,  True,  True,  True,  True, False,  True, False, False,\n",
       "        False,  True, False,  True, False, False, False, False,  True,\n",
       "        False,  True,  True, False, False,  True,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True,  True, False, False, False,\n",
       "        False,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False,  True,  True, False,\n",
       "         True,  True,  True, False,  True, False,  True,  True,  True,\n",
       "         True,  True, False,  True, False, False, False, False,  True,\n",
       "        False,  True,  True, False, False,  True,  True,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True,  True, False,  True, False,\n",
       "         True,  True,  True, False,  True, False,  True,  True, False,\n",
       "         True,  True, False,  True, False, False, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True,  True,  True, False,\n",
       "         True, False, False,  True,  True,  True, False,  True,  True,\n",
       "        False,  True, False,  True,  True,  True, False]),\n",
       " array([ True, False,  True,  True,  True, False,  True,  True,  True,\n",
       "         True,  True, False,  True,  True, False,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False,  True,  True, False, False,  True,\n",
       "         True,  True, False, False, False,  True, False,  True,  True,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False,  True, False,  True,\n",
       "         True,  True,  True, False,  True, False,  True, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False, False, False, False, False,\n",
       "         True,  True,  True,  True,  True, False, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True, False,  True,  True, False,  True, False,\n",
       "         True,  True,  True,  True,  True,  True, False, False, False,\n",
       "        False,  True, False,  True, False, False, False, False,  True,\n",
       "        False,  True,  True, False, False, False,  True,  True,  True,\n",
       "        False, False,  True,  True, False,  True,  True, False, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False,  True, False,  True,\n",
       "         True,  True,  True, False,  True, False,  True, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False, False, False,  True, False,\n",
       "         True,  True,  True,  True,  True, False, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True,  True, False,  True,  True,\n",
       "         True,  True,  True,  True,  True, False,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True, False,  True, False,\n",
       "         True, False, False,  True,  True, False, False, False, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True, False, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True, False,  True,  True, False,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False,  True,  True, False, False,  True,\n",
       "         True,  True, False, False, False,  True, False,  True,  True,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True, False,  True,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False, False,  True, False,  True,  True,\n",
       "         True, False, False,  True,  True,  True,  True, False, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True,  True,  True,  True, False,\n",
       "         True,  True,  True, False,  True, False,  True, False,  True,\n",
       "         True,  True, False,  True, False, False, False,  True,  True,\n",
       "        False,  True,  True, False,  True,  True, False,  True, False,\n",
       "        False,  True,  True,  True,  True,  True, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True, False,  True, False,  True,\n",
       "         True,  True,  True, False,  True, False,  True, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True,  True, False, False, False,  True, False,\n",
       "         True,  True, False,  True,  True, False, False,  True, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False,  True, False,  True,\n",
       "         True,  True,  True, False,  True, False,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True,  True, False,  True, False,  True,  True,\n",
       "        False,  True, False,  True,  True, False, False, False, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True, False,  True, False,  True,  True,  True,\n",
       "         True,  True,  True,  True,  True, False,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False,  True, False,  True,  True,  True,\n",
       "        False,  True, False,  True, False,  True,  True,  True, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True, False,  True, False,  True,  True, False,  True,  True,\n",
       "         True,  True,  True, False,  True, False,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False,  True, False,  True,  True,  True,\n",
       "        False,  True, False,  True, False,  True,  True,  True, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True,  True,  True, False,  True,\n",
       "         True,  True,  True, False,  True,  True,  True, False,  True,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False,  True, False, False,  True, False,\n",
       "         True,  True,  True,  True,  True, False, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True, False,  True, False,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True, False,  True, False,\n",
       "         True, False, False,  True,  True, False, False, False,  True,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False,  True,  True, False,\n",
       "         True,  True, False,  True,  True, False, False, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True, False,  True,  True,  True,  True,  True,\n",
       "         True, False,  True,  True,  True, False, False,  True,  True,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False, False, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True, False,\n",
       "         True,  True, False,  True, False, False, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True,  True,  True,  True,\n",
       "        False,  True, False,  True, False,  True, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True,  True,  True, False, False,  True, False,\n",
       "         True,  True, False,  True,  True,  True,  True,  True, False,\n",
       "         True,  True, False,  True, False, False, False,  True,  True,\n",
       "         True,  True,  True,  True, False,  True, False,  True,  True,\n",
       "        False,  True,  True,  True, False, False,  True,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True, False, False,  True,  True,\n",
       "         True,  True, False,  True,  True, False,  True, False,  True,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False,  True, False, False,  True, False,\n",
       "        False, False, False,  True, False,  True, False,  True,  True,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False, False,  True,  True,\n",
       "         True,  True,  True,  True,  True, False,  True, False,  True,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False,  True,  True, False,  True, False,\n",
       "        False, False, False,  True, False,  True, False,  True,  True,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True,  True, False, False,  True,\n",
       "         True,  True,  True, False,  True, False, False, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False, False, False,  True,  True,\n",
       "         True, False, False,  True,  True, False, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True,  True,  True,  True, False,  True, False,\n",
       "         True,  True, False, False,  True,  True,  True,  True, False,\n",
       "         True,  True, False,  True, False, False, False,  True,  True,\n",
       "         True,  True,  True,  True, False,  True, False,  True,  True,\n",
       "        False,  True,  True,  True, False, False,  True,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False, False, False,  True,\n",
       "         True,  True,  True, False,  True, False,  True, False,  True,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True,  True, False,  True, False,  True, False,\n",
       "        False,  True, False,  True, False, False, False, False, False,\n",
       "        False,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True,  True,  True, False,  True, False,  True,\n",
       "         True,  True,  True,  True,  True, False,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False, False, False,  True, False,\n",
       "         True,  True,  True,  True,  True, False, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True,  True,  True, False,  True,\n",
       "         True,  True,  True, False,  True, False,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True,  True,  True,  True, False,  True,  True,\n",
       "        False,  True, False,  True,  True, False, False,  True, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True,  True, False, False,  True,\n",
       "         True,  True,  True,  True,  True, False,  True,  True,  True,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False,  True,  True, False,  True, False,\n",
       "        False, False, False,  True, False,  True, False,  True,  True,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True,  True, False, False,  True,\n",
       "         True,  True,  True, False,  True,  True,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False, False,  True,\n",
       "        False,  True,  True, False, False, False, False,  True, False,\n",
       "         True,  True,  True,  True,  True, False, False,  True, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True,  True, False, False,  True,\n",
       "         True,  True,  True,  True,  True, False,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True,  True, False,  True,  True,  True,  True,\n",
       "        False,  True, False,  True,  True,  True, False, False, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True,  True,  True, False,  True, False,  True,\n",
       "         True,  True,  True,  True,  True, False, False, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False, False, False,  True, False,\n",
       "         True,  True,  True,  True,  True, False, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True,  True, False, False,  True,\n",
       "         True,  True,  True,  True,  True, False, False,  True, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False, False, False,  True,  True,\n",
       "         True,  True,  True,  True,  True, False, False, False, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True,  True, False,  True,  True,\n",
       "         True,  True, False,  True,  True, False,  True, False,  True,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False, False, False,  True, False,\n",
       "        False, False, False,  True, False,  True, False,  True,  True,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True,  True,  True,  True, False, False, False,\n",
       "         True,  True,  True, False,  True,  True,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True,  True, False,  True, False,  True,  True,\n",
       "        False, False, False,  True,  True, False,  True, False, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True,  True, False, False,  True,\n",
       "         True,  True,  True,  True,  True, False, False,  True, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False, False, False,  True,  True,\n",
       "         True,  True,  True,  True,  True, False, False, False, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True,  True, False, False,  True,\n",
       "         True,  True,  True,  True,  True, False,  True, False,  True,\n",
       "        False,  True, False,  True, False, False, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True, False,  True,  True,\n",
       "         True,  True,  True, False,  True, False, False,  True, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True,  True,  True, False, False, False,  True,\n",
       "         True,  True,  True, False,  True,  True,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True,  True,  True,  True,  True,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False, False, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True,  True,  True, False,  True, False, False,\n",
       "         True,  True,  True, False,  True,  True,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True,  True, False, False, False,  True,  True,\n",
       "        False, False, False,  True,  True, False,  True, False, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True, False,  True,  True, False, False, False,\n",
       "         True,  True,  True,  True,  True, False,  True, False,  True,\n",
       "         True,  True, False,  True, False, False, False,  True,  True,\n",
       "        False,  True,  True,  True, False,  True,  True,  True,  True,\n",
       "        False,  True, False,  True, False,  True, False,  True,  True,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True,  True,  True, False, False, False,  True,\n",
       "         True,  True,  True, False,  True,  True,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True,  True,  True,  True,  True,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False, False, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False,  True,  True, False,  True,  True,  True,\n",
       "         True,  True, False, False,  True, False,  True, False,  True,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False,  True,  True,  True, False,\n",
       "        False, False, False,  True, False,  True, False, False, False,\n",
       "         True,  True, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True, False,  True,  True, False, False,  True,\n",
       "         True,  True,  True, False,  True,  True,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True,  True,  True,  True,  True,  True, False,\n",
       "         True,  True, False,  True,  True,  True, False, False, False,\n",
       "        False, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True,  True,  True, False, False, False,  True,\n",
       "         True,  True,  True, False,  True, False,  True, False, False,\n",
       "        False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True,  True,  True,  True, False,  True,  True,\n",
       "        False, False, False, False,  True, False, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True, False, False,  True, False, False, False, False,\n",
       "         True,  True,  True,  True,  True, False,  True, False,  True,\n",
       "         True,  True, False,  True, False, False, False,  True,  True,\n",
       "         True,  True,  True,  True, False, False, False,  True,  True,\n",
       "        False,  True,  True,  True,  True,  True, False, False, False,\n",
       "        False,  True, False,  True,  True,  True, False]),\n",
       " array([ True, False, False, False,  True, False, False, False,  True,\n",
       "         True,  True,  True, False,  True, False, False, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False, False, False, False,  True,  True,\n",
       "         True,  True, False,  True, False, False, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False]),\n",
       " array([ True,  True,  True, False,  True, False,  True, False,  True,\n",
       "         True,  True, False, False,  True, False, False, False, False,\n",
       "         True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        False,  True,  True, False,  True, False, False,  True,  True,\n",
       "         True, False, False,  True,  True, False, False,  True, False,\n",
       "         True, False, False,  True,  True,  True, False])]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chromo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_RST', 'TCP_PSH', 'TCP_window', 'TCP_options', 'UDP_len', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_RST', 'TCP_PSH', 'TCP_window', 'TCP_options', 'UDP_len', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_PSH', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_PSH', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_PSH', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_PSH', 'TCP_window', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_window', 'TCP_options', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_options', 'DNS_qr', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_PSH', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_window', 'TCP_options', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_options', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_secs', 'BOOTP_sname', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_secs', 'BOOTP_sname', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_PSH', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_window', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_PSH', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_sname', 'BOOTP_file', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_DF', 'IP_ttl', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_PSH', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_ttl', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_sname', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_window', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_options', 'DNS_aa', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_ttl', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_ttl', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_ttl', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'IP_tos', 'IP_len', 'IP_ttl', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_secs', 'BOOTP_sname', 'BOOTP_file', 'DNS_qr', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_type', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_sname', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_ttl', 'IP_options', 'ICMP_type', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_nscount', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_ttl', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_secs', 'BOOTP_sname', 'BOOTP_file', 'DNS_qr', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_qr', 'DNS_aa', 'DNS_ra', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_aa', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'Label'], ['pck_size', 'LLC_ctrl', 'EAPOL_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'IP_version', 'IP_tos', 'IP_len', 'IP_DF', 'IP_ttl', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_aa', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_DF', 'IP_ttl', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_ra', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'TCP_dataofs', 'TCP_FIN', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'DNS_qr', 'DNS_aa', 'DNS_ra', 'DNS_rcode', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_type', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'LLC_ctrl', 'EAPOL_version', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_DF', 'IP_ttl', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_ra', 'DNS_qdcount', 'DNS_ancount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_DF', 'IP_ttl', 'IP_proto', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_len', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_qdcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_options', 'DNS_aa', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_options', 'DNS_aa', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_ancount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_len', 'IP_DF', 'IP_ttl', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'IP_tos', 'IP_len', 'IP_DF', 'IP_ttl', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_options', 'DNS_aa', 'DNS_ra', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'IP_version', 'IP_tos', 'IP_len', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_flags', 'DNS_aa', 'DNS_ra', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_flags', 'DNS_aa', 'DNS_ra', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_version', 'IP_tos', 'IP_len', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_options', 'DNS_aa', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_qdcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_type', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_flags', 'DNS_aa', 'DNS_ra', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_type', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_qdcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_flags', 'DNS_aa', 'DNS_ra', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_sname', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_rd', 'DNS_qdcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_flags', 'BOOTP_sname', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_options', 'DNS_aa', 'DNS_ra', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_ttl', 'IP_options', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'DNS_aa', 'DNS_ra', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_options', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_sname', 'DNS_rd', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_ACK', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'EAPOL_version', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_aa', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_len', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_ttl', 'ICMP_seq', 'TCP_dataofs', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_htype', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Label']]\n"
     ]
    }
   ],
   "source": [
    "sonuç=[]\n",
    "for j in chromo:\n",
    "    temp=X_train.iloc[:,j]\n",
    "    temp=list(temp.columns)\n",
    "    temp.append(\"Label\")\n",
    "    sonuç.append(temp)\n",
    "print(sonuç)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MEAN                           STD                           \n",
      "0.7216485209683412             0.00017206169854589853        \n"
     ]
    }
   ],
   "source": [
    "results=[]\n",
    "for i in range(10):\n",
    "    logmodel.fit(X_train.iloc[:,chromo[-1]],y_train)\n",
    "    predictions = logmodel.predict(X_test.iloc[:,chromo[-1]])\n",
    "    results.append(f1_score(y_test,predictions,average= \"macro\"))\n",
    "print ('%-30s %-30s' % (\"MEAN\",\"STD\"))\n",
    "print ('%-30s %-30s' % (np.mean(results),np.std(results)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison of GA results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from  MLs import ML, find_the_way\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def folder(f_name): #this function creates a folder named \"feaure_pics\" in the program directory.\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": 11,
   "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",
      "0__39           0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.0     0.21    0.0     \n",
      "0__39           1   2   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.21     0.01    0.22    0.0     \n",
      "0__39           2   3   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.23     0.01    0.24    0.0     \n",
      "0__39           3   4   DT      0.7   0.7   0.76  0.7   0.7209   0.67  0.22     0.02    0.24    0.0     \n",
      "0__39           4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.22     0.0     0.22    0.0     \n",
      "0__39           5   6   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.22     0.0     0.22    0.0     \n",
      "0__39           6   7   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.22     0.01    0.24    0.0     \n",
      "0__39           7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.24     0.01    0.25    0.0     \n",
      "0__39           8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "0__39           9   10  DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.21     0.01    0.22    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "1__40           0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.22     0.01    0.23    0.0     \n",
      "1__40           1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "1__40           2   3   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.24     0.01    0.25    0.0     \n",
      "1__40           3   4   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.22     0.01    0.23    0.0     \n",
      "1__40           4   5   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.24     0.01    0.25    0.0     \n",
      "1__40           5   6   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "1__40           6   7   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.21     0.01    0.22    0.0     \n",
      "1__40           7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.01    0.22    0.0     \n",
      "1__40           8   9   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.23     0.01    0.24    0.0     \n",
      "1__40           9   10  DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.21     0.01    0.22    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "2__38           0   1   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.21    0.0     \n",
      "2__38           1   2   DT      0.7   0.7   0.76  0.7   0.721    0.67  0.22     0.01    0.23    0.0     \n",
      "2__38           2   3   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.26     0.01    0.26    0.0     \n",
      "2__38           3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.22     0.01    0.22    0.0     \n",
      "2__38           4   5   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.21     0.01    0.22    0.0     \n",
      "2__38           5   6   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "2__38           6   7   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.21    0.0     \n",
      "2__38           7   8   DT      0.7   0.7   0.76  0.7   0.721    0.67  0.21     0.01    0.21    0.0     \n",
      "2__38           8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "2__38           9   10  DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "3__38           0   1   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.2      0.01    0.21    0.0     \n",
      "3__38           1   2   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.2      0.01    0.21    0.0     \n",
      "3__38           2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "3__38           3   4   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.25     0.01    0.25    0.0     \n",
      "3__38           4   5   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.21     0.01    0.21    0.0     \n",
      "3__38           5   6   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.21     0.01    0.21    0.0     \n",
      "3__38           6   7   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.2      0.01    0.21    0.0     \n",
      "3__38           7   8   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "3__38           8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "3__38           9   10  DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "4__38           0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "4__38           1   2   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.21     0.01    0.21    0.0     \n",
      "4__38           2   3   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.01    0.21    0.0     \n",
      "4__38           3   4   DT      0.7   0.7   0.76  0.7   0.7213   0.67  0.2      0.01    0.21    0.0     \n",
      "4__38           4   5   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "4__38           5   6   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "4__38           6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.21     0.01    0.21    0.0     \n",
      "4__38           7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "4__38           8   9   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.21     0.01    0.21    0.0     \n",
      "4__38           9   10  DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.21     0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "5__37           0   1   DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.21     0.01    0.22    0.0     \n",
      "5__37           1   2   DT      0.7   0.7   0.76  0.7   0.7211   0.68  0.2      0.01    0.21    0.0     \n",
      "5__37           2   3   DT      0.7   0.7   0.76  0.7   0.7211   0.68  0.2      0.01    0.21    0.0     \n",
      "5__37           3   4   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "5__37           4   5   DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.2      0.01    0.21    0.0     \n",
      "5__37           5   6   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "5__37           6   7   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.21     0.01    0.21    0.0     \n",
      "5__37           7   8   DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.2      0.01    0.21    0.0     \n",
      "5__37           8   9   DT      0.7   0.7   0.76  0.7   0.7208   0.68  0.21     0.01    0.21    0.0     \n",
      "5__37           9   10  DT      0.7   0.7   0.76  0.7   0.721    0.67  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "6__39           0   1   DT      0.7   0.7   0.76  0.7   0.7213   0.67  0.2      0.01    0.21    0.0     \n",
      "6__39           1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "6__39           2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "6__39           3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "6__39           4   5   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.2      0.01    0.21    0.0     \n",
      "6__39           5   6   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.01    0.21    0.0     \n",
      "6__39           6   7   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.21     0.01    0.21    0.0     \n",
      "6__39           7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "6__39           8   9   DT      0.7   0.7   0.76  0.7   0.7211   0.68  0.2      0.01    0.21    0.0     \n",
      "6__39           9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "7__37           0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7__37           1   2   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.2      0.01    0.21    0.0     \n",
      "7__37           2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.67  0.19     0.01    0.2     0.0     \n",
      "7__37           3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.0     0.21    0.0     \n",
      "7__37           4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "7__37           5   6   DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.21     0.0     0.21    0.0     \n",
      "7__37           6   7   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.0     0.2     0.0     \n",
      "7__37           7   8   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.0     0.2     0.0     \n",
      "7__37           8   9   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.19     0.01    0.2     0.0     \n",
      "7__37           9   10  DT      0.7   0.7   0.76  0.7   0.7213   0.67  0.21     0.01    0.22    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "8__41           0   1   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.21     0.01    0.22    0.0     \n",
      "8__41           1   2   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "8__41           2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "8__41           3   4   DT      0.7   0.7   0.76  0.7   0.721    0.67  0.21     0.01    0.22    0.0     \n",
      "8__41           4   5   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.21     0.01    0.22    0.0     \n",
      "8__41           5   6   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "8__41           6   7   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.21     0.01    0.22    0.0     \n",
      "8__41           7   8   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.21     0.01    0.22    0.0     \n",
      "8__41           8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.23     0.01    0.23    0.0     \n",
      "8__41           9   10  DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.21     0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "9__36           0   1   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "9__36           1   2   DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.2      0.01    0.21    0.0     \n",
      "9__36           2   3   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.19     0.01    0.2     0.0     \n",
      "9__36           3   4   DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.2      0.01    0.2     0.0     \n",
      "9__36           4   5   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.19     0.01    0.2     0.0     \n",
      "9__36           5   6   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "9__36           6   7   DT      0.7   0.7   0.76  0.7   0.7211   0.68  0.2      0.01    0.2     0.0     \n",
      "9__36           7   8   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "9__36           8   9   DT      0.7   0.7   0.76  0.7   0.7212   0.67  0.2      0.01    0.2     0.0     \n",
      "9__36           9   10  DT      0.7   0.7   0.76  0.7   0.7207   0.67  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "10__36          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.2     0.0     \n",
      "10__36          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "10__36          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.2     0.0     \n",
      "10__36          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "10__36          4   5   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.2      0.01    0.2     0.0     \n",
      "10__36          5   6   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "10__36          6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.2      0.01    0.2     0.0     \n",
      "10__36          7   8   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "10__36          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.2     0.0     \n",
      "10__36          9   10  DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "11__36          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "11__36          1   2   DT      0.7   0.7   0.76  0.7   0.721    0.67  0.2      0.01    0.21    0.0     \n",
      "11__36          2   3   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.01    0.2     0.0     \n",
      "11__36          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "11__36          4   5   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "11__36          5   6   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "11__36          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "11__36          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "11__36          8   9   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.19     0.01    0.2     0.0     \n",
      "11__36          9   10  DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "12__39          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "12__39          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.21    0.0     \n",
      "12__39          2   3   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "12__39          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "12__39          4   5   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.2      0.01    0.21    0.0     \n",
      "12__39          5   6   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.21     0.01    0.21    0.0     \n",
      "12__39          6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.2      0.01    0.21    0.0     \n",
      "12__39          7   8   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.2      0.01    0.21    0.0     \n",
      "12__39          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "12__39          9   10  DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.21     0.01    0.22    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "13__39          0   1   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.01    0.21    0.0     \n",
      "13__39          1   2   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "13__39          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.25     0.01    0.26    0.0     \n",
      "13__39          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "13__39          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.22     0.01    0.23    0.0     \n",
      "13__39          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "13__39          6   7   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "13__39          7   8   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.2      0.01    0.2     0.0     \n",
      "13__39          8   9   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.01    0.21    0.0     \n",
      "13__39          9   10  DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "14__40          0   1   DT      0.7   0.7   0.75  0.7   0.7139   0.67  0.19     0.01    0.2     0.0     \n",
      "14__40          1   2   DT      0.7   0.7   0.75  0.7   0.714    0.67  0.19     0.01    0.2     0.0     \n",
      "14__40          2   3   DT      0.7   0.7   0.75  0.7   0.714    0.67  0.2      0.02    0.22    0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14__40          3   4   DT      0.7   0.7   0.75  0.7   0.7142   0.67  0.2      0.0     0.2     0.0     \n",
      "14__40          4   5   DT      0.7   0.7   0.75  0.7   0.714    0.67  0.2      0.0     0.2     0.0     \n",
      "14__40          5   6   DT      0.7   0.7   0.75  0.7   0.7138   0.67  0.19     0.01    0.2     0.0     \n",
      "14__40          6   7   DT      0.7   0.7   0.75  0.7   0.7134   0.67  0.19     0.01    0.2     0.0     \n",
      "14__40          7   8   DT      0.7   0.7   0.75  0.7   0.7141   0.67  0.2      0.0     0.2     0.0     \n",
      "14__40          8   9   DT      0.7   0.7   0.75  0.7   0.7142   0.67  0.2      0.0     0.2     0.0     \n",
      "14__40          9   10  DT      0.7   0.7   0.75  0.7   0.714    0.67  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "15__36          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "15__36          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "15__36          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "15__36          3   4   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.19     0.01    0.2     0.0     \n",
      "15__36          4   5   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.2      0.0     0.2     0.0     \n",
      "15__36          5   6   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.0     0.21    0.0     \n",
      "15__36          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.2     0.0     \n",
      "15__36          7   8   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.01    0.2     0.0     \n",
      "15__36          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "15__36          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "16__36          0   1   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.19     0.01    0.2     0.0     \n",
      "16__36          1   2   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.19     0.01    0.2     0.0     \n",
      "16__36          2   3   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.19     0.01    0.2     0.0     \n",
      "16__36          3   4   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.0     0.2     0.0     \n",
      "16__36          4   5   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "16__36          5   6   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.0     0.2     0.0     \n",
      "16__36          6   7   DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.19     0.01    0.2     0.0     \n",
      "16__36          7   8   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.19     0.01    0.2     0.0     \n",
      "16__36          8   9   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.21    0.0     \n",
      "16__36          9   10  DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "17__37          0   1   DT      0.7   0.7   0.75  0.7   0.7135   0.67  0.18     0.01    0.19    0.0     \n",
      "17__37          1   2   DT      0.7   0.7   0.75  0.7   0.7141   0.67  0.19     0.0     0.2     0.0     \n",
      "17__37          2   3   DT      0.7   0.7   0.75  0.7   0.714    0.67  0.18     0.0     0.18    0.0     \n",
      "17__37          3   4   DT      0.7   0.7   0.75  0.7   0.714    0.67  0.19     0.01    0.19    0.0     \n",
      "17__37          4   5   DT      0.7   0.7   0.75  0.7   0.714    0.67  0.17     0.02    0.18    0.0     \n",
      "17__37          5   6   DT      0.7   0.7   0.75  0.7   0.714    0.67  0.22     0.01    0.23    0.0     \n",
      "17__37          6   7   DT      0.7   0.7   0.75  0.7   0.714    0.67  0.18     0.01    0.19    0.0     \n",
      "17__37          7   8   DT      0.7   0.7   0.75  0.7   0.7141   0.67  0.18     0.0     0.18    0.0     \n",
      "17__37          8   9   DT      0.7   0.7   0.75  0.7   0.7136   0.67  0.18     0.01    0.19    0.0     \n",
      "17__37          9   10  DT      0.7   0.7   0.75  0.7   0.7139   0.67  0.18     0.01    0.19    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "18__38          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.02    0.21    0.0     \n",
      "18__38          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.0     0.21    0.0     \n",
      "18__38          2   3   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.01    0.21    0.0     \n",
      "18__38          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.67  0.2      0.01    0.21    0.0     \n",
      "18__38          4   5   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.02    0.23    0.0     \n",
      "18__38          5   6   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.22     0.0     0.23    0.0     \n",
      "18__38          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "18__38          7   8   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.22    0.0     \n",
      "18__38          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "18__38          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.0     0.22    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "19__37          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.23     0.02    0.25    0.0     \n",
      "19__37          1   2   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.23     0.01    0.24    0.0     \n",
      "19__37          2   3   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.19     0.0     0.19    0.0     \n",
      "19__37          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.0     0.21    0.0     \n",
      "19__37          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "19__37          5   6   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.18     0.02    0.2     0.0     \n",
      "19__37          6   7   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.21    0.0     \n",
      "19__37          7   8   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.22     0.01    0.23    0.0     \n",
      "19__37          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "19__37          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "20__37          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.21    0.0     \n",
      "20__37          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.25     0.01    0.26    0.0     \n",
      "20__37          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.24     0.01    0.25    0.0     \n",
      "20__37          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n",
      "20__37          4   5   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.23     0.01    0.24    0.0     \n",
      "20__37          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.23     0.01    0.23    0.0     \n",
      "20__37          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.22    0.0     \n",
      "20__37          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "20__37          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "20__37          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "21__32          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.01    0.19    0.0     \n",
      "21__32          1   2   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.0     0.19    0.0     \n",
      "21__32          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.01    0.19    0.0     \n",
      "21__32          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.18     0.02    0.2     0.0     \n",
      "21__32          4   5   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.19     0.0     0.19    0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21__32          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "21__32          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.18     0.01    0.19    0.0     \n",
      "21__32          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.0     0.19    0.0     \n",
      "21__32          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.18     0.01    0.19    0.0     \n",
      "21__32          9   10  DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.0     0.19    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "22__37          0   1   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "22__37          1   2   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.22     0.0     0.22    0.0     \n",
      "22__37          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.0     0.19    0.0     \n",
      "22__37          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.02    0.2     0.0     \n",
      "22__37          4   5   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.0     0.2     0.0     \n",
      "22__37          5   6   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n",
      "22__37          6   7   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.21    0.0     \n",
      "22__37          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "22__37          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "22__37          9   10  DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.0     0.19    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "23__37          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "23__37          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "23__37          2   3   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.0     0.2     0.0     \n",
      "23__37          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.0     0.21    0.0     \n",
      "23__37          4   5   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.01    0.21    0.0     \n",
      "23__37          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.02    0.22    0.0     \n",
      "23__37          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "23__37          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.21    0.0     \n",
      "23__37          8   9   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.01    0.21    0.0     \n",
      "23__37          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "24__38          0   1   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.01    0.2     0.0     \n",
      "24__38          1   2   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "24__38          2   3   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.21    0.0     \n",
      "24__38          3   4   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.2      0.01    0.21    0.0     \n",
      "24__38          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "24__38          5   6   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.21    0.0     \n",
      "24__38          6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.01    0.21    0.0     \n",
      "24__38          7   8   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.2     0.0     \n",
      "24__38          8   9   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.2     0.0     \n",
      "24__38          9   10  DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.21     0.01    0.22    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "25__38          0   1   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.23     0.01    0.23    0.0     \n",
      "25__38          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.24     0.01    0.25    0.0     \n",
      "25__38          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.21    0.0     \n",
      "25__38          3   4   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.23     0.01    0.23    0.0     \n",
      "25__38          4   5   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.21     0.01    0.21    0.0     \n",
      "25__38          5   6   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.21     0.01    0.22    0.0     \n",
      "25__38          6   7   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "25__38          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.01    0.23    0.0     \n",
      "25__38          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "25__38          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "26__34          0   1   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.18     0.01    0.19    0.0     \n",
      "26__34          1   2   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.19     0.01    0.2     0.0     \n",
      "26__34          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.01    0.19    0.0     \n",
      "26__34          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.18     0.01    0.19    0.0     \n",
      "26__34          4   5   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.18     0.01    0.19    0.0     \n",
      "26__34          5   6   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "26__34          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.18     0.01    0.19    0.0     \n",
      "26__34          7   8   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.18     0.01    0.19    0.0     \n",
      "26__34          8   9   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.18     0.01    0.19    0.0     \n",
      "26__34          9   10  DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "27__33          0   1   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.2     0.0     \n",
      "27__33          1   2   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.01    0.2     0.0     \n",
      "27__33          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "27__33          3   4   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.2     0.0     \n",
      "27__33          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.2     0.0     \n",
      "27__33          5   6   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.19     0.01    0.2     0.0     \n",
      "27__33          6   7   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.22     0.01    0.22    0.0     \n",
      "27__33          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.23     0.01    0.23    0.0     \n",
      "27__33          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.22     0.01    0.23    0.0     \n",
      "27__33          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "28__37          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.01    0.22    0.0     \n",
      "28__37          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.01    0.23    0.0     \n",
      "28__37          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "28__37          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.2     0.0     \n",
      "28__37          4   5   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.0     0.2     0.0     \n",
      "28__37          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.0     0.22    0.0     \n",
      "28__37          6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.2      0.01    0.21    0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28__37          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "28__37          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "28__37          9   10  DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "29__34          0   1   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.19     0.0     0.19    0.0     \n",
      "29__34          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "29__34          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "29__34          3   4   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.23     0.0     0.24    0.0     \n",
      "29__34          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.22     0.01    0.23    0.0     \n",
      "29__34          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "29__34          6   7   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "29__34          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "29__34          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.33     0.01    0.34    0.0     \n",
      "29__34          9   10  DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "30__35          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.25     0.01    0.26    0.0     \n",
      "30__35          1   2   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.29     0.01    0.29    0.0     \n",
      "30__35          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "30__35          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "30__35          4   5   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.02    0.22    0.0     \n",
      "30__35          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.02    0.2     0.0     \n",
      "30__35          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.22    0.0     \n",
      "30__35          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.18     0.02    0.2     0.0     \n",
      "30__35          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "30__35          9   10  DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "31__35          0   1   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.2     0.0     \n",
      "31__35          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.21    0.0     \n",
      "31__35          2   3   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.0     0.2     0.0     \n",
      "31__35          3   4   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.19     0.01    0.2     0.0     \n",
      "31__35          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "31__35          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.21    0.0     \n",
      "31__35          6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.01    0.2     0.0     \n",
      "31__35          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "31__35          8   9   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.22     0.01    0.22    0.0     \n",
      "31__35          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "32__33          0   1   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.19     0.01    0.2     0.0     \n",
      "32__33          1   2   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.19     0.01    0.2     0.0     \n",
      "32__33          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "32__33          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "32__33          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.2     0.0     \n",
      "32__33          5   6   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.27     0.01    0.27    0.0     \n",
      "32__33          6   7   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.0     0.19    0.0     \n",
      "32__33          7   8   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.18     0.01    0.19    0.0     \n",
      "32__33          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.25     0.01    0.26    0.0     \n",
      "32__33          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "33__37          0   1   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "33__37          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "33__37          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.23     0.02    0.25    0.0     \n",
      "33__37          3   4   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "33__37          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.02    0.22    0.0     \n",
      "33__37          5   6   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.21     0.01    0.22    0.0     \n",
      "33__37          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "33__37          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.02    0.22    0.0     \n",
      "33__37          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.22     0.01    0.22    0.0     \n",
      "33__37          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.0     0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "34__38          0   1   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.21     0.0     0.21    0.0     \n",
      "34__38          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "34__38          2   3   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.22    0.0     \n",
      "34__38          3   4   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.21     0.0     0.21    0.0     \n",
      "34__38          4   5   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "34__38          5   6   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.21     0.0     0.21    0.0     \n",
      "34__38          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.29     0.01    0.3     0.0     \n",
      "34__38          7   8   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.0     0.2     0.0     \n",
      "34__38          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.02    0.22    0.0     \n",
      "34__38          9   10  DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.0     0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "35__36          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "35__36          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.23     0.01    0.23    0.0     \n",
      "35__36          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.22     0.01    0.23    0.0     \n",
      "35__36          3   4   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.02    0.22    0.0     \n",
      "35__36          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.23     0.01    0.24    0.0     \n",
      "35__36          5   6   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.23     0.0     0.24    0.0     \n",
      "35__36          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.21    0.0     \n",
      "35__36          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "35__36          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.23     0.01    0.23    0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35__36          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "36__33          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.01    0.23    0.0     \n",
      "36__33          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.29     0.01    0.29    0.0     \n",
      "36__33          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.02    0.21    0.0     \n",
      "36__33          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.02    0.2     0.0     \n",
      "36__33          4   5   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.19     0.02    0.21    0.0     \n",
      "36__33          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.02    0.22    0.0     \n",
      "36__33          6   7   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.0     0.21    0.0     \n",
      "36__33          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.24     0.02    0.25    0.0     \n",
      "36__33          8   9   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.31     0.01    0.32    0.0     \n",
      "36__33          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.26     0.02    0.28    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "37__34          0   1   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.28     0.02    0.3     0.0     \n",
      "37__34          1   2   DT      0.7   0.7   0.76  0.7   0.721    0.68  0.2      0.0     0.2     0.0     \n",
      "37__34          2   3   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.23     0.01    0.24    0.0     \n",
      "37__34          3   4   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.25     0.01    0.25    0.0     \n",
      "37__34          4   5   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.19     0.01    0.2     0.0     \n",
      "37__34          5   6   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.19     0.01    0.2     0.0     \n",
      "37__34          6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.19     0.02    0.2     0.0     \n",
      "37__34          7   8   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.24     0.01    0.25    0.0     \n",
      "37__34          8   9   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.22     0.02    0.23    0.0     \n",
      "37__34          9   10  DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.2      0.02    0.22    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "38__35          0   1   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "38__35          1   2   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.36     0.01    0.37    0.0     \n",
      "38__35          2   3   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.2      0.0     0.2     0.0     \n",
      "38__35          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "38__35          4   5   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.01    0.22    0.0     \n",
      "38__35          5   6   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.21     0.01    0.22    0.0     \n",
      "38__35          6   7   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.2     0.0     \n",
      "38__35          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "38__35          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "38__35          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "39__38          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "39__38          1   2   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.22    0.0     \n",
      "39__38          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.21    0.0     \n",
      "39__38          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "39__38          4   5   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.21    0.0     \n",
      "39__38          5   6   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.22    0.0     \n",
      "39__38          6   7   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.21     0.01    0.22    0.0     \n",
      "39__38          7   8   DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.21     0.01    0.21    0.0     \n",
      "39__38          8   9   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.21     0.01    0.22    0.0     \n",
      "39__38          9   10  DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "40__36          0   1   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.21    0.0     \n",
      "40__36          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "40__36          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.2     0.0     \n",
      "40__36          3   4   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.2     0.0     \n",
      "40__36          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.01    0.2     0.0     \n",
      "40__36          5   6   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.2     0.0     \n",
      "40__36          6   7   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.2     0.0     \n",
      "40__36          7   8   DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.21     0.01    0.22    0.0     \n",
      "40__36          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.2     0.0     \n",
      "40__36          9   10  DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.2      0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "41__36          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "41__36          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "41__36          2   3   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.21     0.01    0.22    0.0     \n",
      "41__36          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "41__36          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.67  0.21     0.01    0.21    0.0     \n",
      "41__36          5   6   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.21     0.01    0.21    0.0     \n",
      "41__36          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "41__36          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "41__36          8   9   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.21     0.01    0.21    0.0     \n",
      "41__36          9   10  DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "42__34          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "42__34          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.2     0.0     \n",
      "42__34          2   3   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.22     0.01    0.23    0.0     \n",
      "42__34          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "42__34          4   5   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.21    0.0     \n",
      "42__34          5   6   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.21     0.01    0.22    0.0     \n",
      "42__34          6   7   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.2     0.0     \n",
      "42__34          7   8   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.26     0.01    0.27    0.0     \n",
      "42__34          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "42__34          9   10  DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43__36          0   1   DT      0.67  0.6   0.66  0.6   0.6124   0.63  0.13     0.01    0.14    0.0     \n",
      "43__36          1   2   DT      0.67  0.6   0.66  0.6   0.6125   0.63  0.14     0.01    0.15    0.0     \n",
      "43__36          2   3   DT      0.67  0.6   0.66  0.6   0.6125   0.63  0.14     0.01    0.14    0.0     \n",
      "43__36          3   4   DT      0.67  0.6   0.66  0.6   0.6124   0.63  0.17     0.01    0.17    0.0     \n",
      "43__36          4   5   DT      0.67  0.6   0.66  0.6   0.6124   0.63  0.14     0.01    0.15    0.0     \n",
      "43__36          5   6   DT      0.67  0.6   0.66  0.6   0.6125   0.63  0.14     0.01    0.15    0.0     \n",
      "43__36          6   7   DT      0.67  0.6   0.66  0.6   0.6124   0.63  0.14     0.01    0.15    0.0     \n",
      "43__36          7   8   DT      0.67  0.6   0.66  0.6   0.6124   0.63  0.14     0.01    0.14    0.0     \n",
      "43__36          8   9   DT      0.67  0.6   0.66  0.6   0.6124   0.63  0.14     0.01    0.14    0.0     \n",
      "43__36          9   10  DT      0.67  0.6   0.66  0.6   0.6124   0.63  0.14     0.01    0.14    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "44__35          0   1   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.23     0.01    0.24    0.0     \n",
      "44__35          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.26     0.01    0.27    0.0     \n",
      "44__35          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.23     0.01    0.24    0.0     \n",
      "44__35          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.23     0.01    0.24    0.0     \n",
      "44__35          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "44__35          5   6   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "44__35          6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.01    0.2     0.0     \n",
      "44__35          7   8   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.24     0.01    0.25    0.0     \n",
      "44__35          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.24     0.01    0.25    0.0     \n",
      "44__35          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "45__32          0   1   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "45__32          1   2   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.18     0.01    0.19    0.0     \n",
      "45__32          2   3   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.02    0.22    0.0     \n",
      "45__32          3   4   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.22     0.0     0.22    0.0     \n",
      "45__32          4   5   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.22     0.0     0.22    0.0     \n",
      "45__32          5   6   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.22     0.03    0.25    0.0     \n",
      "45__32          6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.22     0.0     0.22    0.0     \n",
      "45__32          7   8   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.0     0.2     0.0     \n",
      "45__32          8   9   DT      0.7   0.7   0.76  0.7   0.7211   0.68  0.18     0.02    0.2     0.0     \n",
      "45__32          9   10  DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.29     0.02    0.3     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "46__37          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.28     0.01    0.29    0.0     \n",
      "46__37          1   2   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.27     0.01    0.28    0.0     \n",
      "46__37          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.26     0.01    0.27    0.0     \n",
      "46__37          3   4   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.23     0.01    0.24    0.0     \n",
      "46__37          4   5   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.22     0.01    0.23    0.0     \n",
      "46__37          5   6   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "46__37          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.21    0.0     \n",
      "46__37          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "46__37          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "46__37          9   10  DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.21     0.01    0.22    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "47__36          0   1   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.23     0.01    0.24    0.0     \n",
      "47__36          1   2   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.21     0.01    0.21    0.0     \n",
      "47__36          2   3   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.22    0.0     \n",
      "47__36          3   4   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.01    0.21    0.0     \n",
      "47__36          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.21    0.0     \n",
      "47__36          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "47__36          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.22     0.01    0.22    0.0     \n",
      "47__36          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.01    0.23    0.0     \n",
      "47__36          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.2     0.0     \n",
      "47__36          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "48__37          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.23     0.01    0.24    0.0     \n",
      "48__37          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.24     0.01    0.26    0.0     \n",
      "48__37          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.22     0.01    0.23    0.0     \n",
      "48__37          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.23     0.01    0.24    0.0     \n",
      "48__37          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.22     0.01    0.23    0.0     \n",
      "48__37          5   6   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.22     0.01    0.22    0.0     \n",
      "48__37          6   7   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.21    0.0     \n",
      "48__37          7   8   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.24     0.01    0.24    0.0     \n",
      "48__37          8   9   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.21     0.01    0.22    0.0     \n",
      "48__37          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.0     0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "49__37          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.24     0.01    0.25    0.0     \n",
      "49__37          1   2   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.27     0.01    0.28    0.0     \n",
      "49__37          2   3   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.26     0.01    0.27    0.0     \n",
      "49__37          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.02    0.23    0.0     \n",
      "49__37          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.26     0.02    0.27    0.0     \n",
      "49__37          5   6   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.02    0.21    0.0     \n",
      "49__37          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "49__37          7   8   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.0     0.2     0.0     \n",
      "49__37          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.22     0.01    0.23    0.0     \n",
      "49__37          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.23    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "50__31          0   1   DT      0.68  0.65  0.73  0.65  0.6786   0.64  0.19     0.01    0.2     0.0     \n",
      "50__31          1   2   DT      0.68  0.65  0.73  0.65  0.6788   0.64  0.2      0.0     0.2     0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50__31          2   3   DT      0.68  0.65  0.73  0.65  0.6786   0.64  0.19     0.02    0.21    0.0     \n",
      "50__31          3   4   DT      0.68  0.65  0.73  0.65  0.6788   0.64  0.32     0.01    0.33    0.0     \n",
      "50__31          4   5   DT      0.68  0.65  0.73  0.65  0.6787   0.64  0.21     0.02    0.23    0.0     \n",
      "50__31          5   6   DT      0.68  0.65  0.73  0.65  0.6787   0.64  0.22     0.01    0.22    0.0     \n",
      "50__31          6   7   DT      0.68  0.65  0.73  0.65  0.6788   0.64  0.2      0.01    0.21    0.0     \n",
      "50__31          7   8   DT      0.68  0.65  0.73  0.65  0.6787   0.64  0.2      0.0     0.2     0.0     \n",
      "50__31          8   9   DT      0.68  0.65  0.73  0.65  0.6783   0.64  0.19     0.0     0.19    0.0     \n",
      "50__31          9   10  DT      0.68  0.65  0.73  0.65  0.6788   0.64  0.18     0.0     0.18    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "51__36          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.01    0.23    0.0     \n",
      "51__36          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.02    0.23    0.0     \n",
      "51__36          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.02    0.2     0.0     \n",
      "51__36          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "51__36          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n",
      "51__36          5   6   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.21     0.01    0.22    0.0     \n",
      "51__36          6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.27     0.0     0.27    0.0     \n",
      "51__36          7   8   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.0     0.2     0.0     \n",
      "51__36          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.22     0.01    0.22    0.0     \n",
      "51__36          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.0     0.22    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "52__32          0   1   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.02    0.2     0.0     \n",
      "52__32          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.19    0.0     \n",
      "52__32          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.02    0.2     0.0     \n",
      "52__32          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.03    0.23    0.0     \n",
      "52__32          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "52__32          5   6   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.22     0.01    0.23    0.0     \n",
      "52__32          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.21    0.0     \n",
      "52__32          7   8   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.18     0.02    0.2     0.0     \n",
      "52__32          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "52__32          9   10  DT      0.7   0.7   0.76  0.7   0.721    0.67  0.22     0.01    0.23    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "53__34          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "53__34          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.23     0.01    0.23    0.0     \n",
      "53__34          2   3   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.19     0.0     0.19    0.0     \n",
      "53__34          3   4   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.22    0.0     \n",
      "53__34          4   5   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.21     0.01    0.22    0.0     \n",
      "53__34          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "53__34          6   7   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.0     0.22    0.0     \n",
      "53__34          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.24     0.01    0.24    0.0     \n",
      "53__34          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.23     0.01    0.23    0.0     \n",
      "53__34          9   10  DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "54__34          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.0     0.21    0.0     \n",
      "54__34          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "54__34          2   3   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.21    0.0     \n",
      "54__34          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "54__34          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.21    0.0     \n",
      "54__34          5   6   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "54__34          6   7   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.2     0.0     \n",
      "54__34          7   8   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.21     0.01    0.22    0.0     \n",
      "54__34          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "54__34          9   10  DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "55__35          0   1   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.02    0.22    0.0     \n",
      "55__35          1   2   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.22     0.02    0.24    0.0     \n",
      "55__35          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.01    0.23    0.0     \n",
      "55__35          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.22     0.01    0.22    0.0     \n",
      "55__35          4   5   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "55__35          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.0     0.21    0.0     \n",
      "55__35          6   7   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.22     0.0     0.22    0.0     \n",
      "55__35          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "55__35          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.0     0.21    0.0     \n",
      "55__35          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.22     0.01    0.23    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "56__31          0   1   DT      0.7   0.69  0.74  0.69  0.7027   0.67  0.21     0.01    0.22    0.0     \n",
      "56__31          1   2   DT      0.7   0.69  0.74  0.69  0.7026   0.67  0.2      0.0     0.2     0.0     \n",
      "56__31          2   3   DT      0.7   0.69  0.74  0.69  0.7028   0.67  0.2      0.0     0.2     0.0     \n",
      "56__31          3   4   DT      0.7   0.69  0.74  0.69  0.7027   0.67  0.22     0.0     0.22    0.0     \n",
      "56__31          4   5   DT      0.7   0.69  0.74  0.69  0.7027   0.67  0.23     0.01    0.24    0.0     \n",
      "56__31          5   6   DT      0.7   0.69  0.74  0.69  0.7027   0.67  0.21     0.01    0.22    0.0     \n",
      "56__31          6   7   DT      0.7   0.69  0.74  0.69  0.7027   0.67  0.24     0.01    0.25    0.0     \n",
      "56__31          7   8   DT      0.7   0.69  0.74  0.69  0.7027   0.67  0.19     0.02    0.2     0.0     \n",
      "56__31          8   9   DT      0.7   0.69  0.74  0.69  0.7028   0.67  0.19     0.02    0.21    0.0     \n",
      "56__31          9   10  DT      0.7   0.69  0.74  0.69  0.703    0.67  0.2      0.02    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "57__34          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "57__34          1   2   DT      0.7   0.7   0.76  0.7   0.7212   0.67  0.2      0.0     0.2     0.0     \n",
      "57__34          2   3   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.22     0.0     0.22    0.0     \n",
      "57__34          3   4   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.23     0.01    0.24    0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57__34          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "57__34          5   6   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "57__34          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.27     0.02    0.29    0.0     \n",
      "57__34          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.25     0.02    0.26    0.0     \n",
      "57__34          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.24     0.01    0.26    0.0     \n",
      "57__34          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.0     0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "58__34          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.02    0.2     0.0     \n",
      "58__34          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n",
      "58__34          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "58__34          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.02    0.2     0.0     \n",
      "58__34          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.22     0.01    0.22    0.0     \n",
      "58__34          5   6   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "58__34          6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.22     0.0     0.22    0.0     \n",
      "58__34          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.02    0.22    0.0     \n",
      "58__34          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "58__34          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "59__34          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "59__34          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "59__34          2   3   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.02    0.2     0.0     \n",
      "59__34          3   4   DT      0.7   0.7   0.76  0.7   0.721    0.67  0.19     0.01    0.2     0.0     \n",
      "59__34          4   5   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.0     0.21    0.0     \n",
      "59__34          5   6   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.19     0.01    0.2     0.0     \n",
      "59__34          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.01    0.2     0.0     \n",
      "59__34          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "59__34          8   9   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.0     0.2     0.0     \n",
      "59__34          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.0     0.19    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "60__33          0   1   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.22     0.02    0.23    0.0     \n",
      "60__33          1   2   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.01    0.2     0.0     \n",
      "60__33          2   3   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.0     0.19    0.0     \n",
      "60__33          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "60__33          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n",
      "60__33          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.02    0.22    0.0     \n",
      "60__33          6   7   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "60__33          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.02    0.2     0.0     \n",
      "60__33          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n",
      "60__33          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "61__37          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "61__37          1   2   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.27     0.01    0.28    0.0     \n",
      "61__37          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.22     0.01    0.23    0.0     \n",
      "61__37          3   4   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.22     0.01    0.23    0.0     \n",
      "61__37          4   5   DT      0.7   0.7   0.76  0.7   0.7212   0.67  0.21     0.0     0.21    0.0     \n",
      "61__37          5   6   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.22    0.0     \n",
      "61__37          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.02    0.22    0.0     \n",
      "61__37          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.23     0.01    0.24    0.0     \n",
      "61__37          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.23     0.01    0.24    0.0     \n",
      "61__37          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.23     0.01    0.24    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "62__34          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.01    0.23    0.0     \n",
      "62__34          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.02    0.2     0.0     \n",
      "62__34          2   3   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.21    0.0     \n",
      "62__34          3   4   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.21     0.01    0.22    0.0     \n",
      "62__34          4   5   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "62__34          5   6   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "62__34          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n",
      "62__34          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.02    0.21    0.0     \n",
      "62__34          8   9   DT      0.7   0.7   0.76  0.7   0.7211   0.68  0.19     0.02    0.21    0.0     \n",
      "62__34          9   10  DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.19     0.02    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "63__34          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "63__34          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.0     0.21    0.0     \n",
      "63__34          2   3   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.21    0.0     \n",
      "63__34          3   4   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "63__34          4   5   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.21    0.0     \n",
      "63__34          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "63__34          6   7   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.21     0.01    0.22    0.0     \n",
      "63__34          7   8   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.2      0.0     0.2     0.0     \n",
      "63__34          8   9   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.21    0.0     \n",
      "63__34          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "64__36          0   1   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.19     0.0     0.19    0.0     \n",
      "64__36          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.67  0.19     0.01    0.2     0.0     \n",
      "64__36          2   3   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.19     0.01    0.19    0.0     \n",
      "64__36          3   4   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.18     0.02    0.19    0.0     \n",
      "64__36          4   5   DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.2      0.01    0.21    0.0     \n",
      "64__36          5   6   DT      0.7   0.7   0.76  0.7   0.7213   0.67  0.19     0.02    0.2     0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64__36          6   7   DT      0.7   0.7   0.76  0.7   0.7212   0.67  0.19     0.01    0.2     0.0     \n",
      "64__36          7   8   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.18     0.01    0.19    0.0     \n",
      "64__36          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.67  0.21     0.0     0.21    0.0     \n",
      "64__36          9   10  DT      0.7   0.7   0.76  0.7   0.7213   0.67  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "65__34          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.22     0.01    0.23    0.0     \n",
      "65__34          1   2   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.01    0.21    0.0     \n",
      "65__34          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "65__34          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.02    0.21    0.0     \n",
      "65__34          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.02    0.2     0.0     \n",
      "65__34          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "65__34          6   7   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.02    0.22    0.0     \n",
      "65__34          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.21    0.0     \n",
      "65__34          8   9   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.18     0.02    0.2     0.0     \n",
      "65__34          9   10  DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "66__34          0   1   DT      0.7   0.7   0.76  0.7   0.7213   0.67  0.23     0.01    0.24    0.0     \n",
      "66__34          1   2   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.23     0.01    0.24    0.0     \n",
      "66__34          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.18     0.02    0.19    0.0     \n",
      "66__34          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.18     0.0     0.18    0.0     \n",
      "66__34          4   5   DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.18     0.02    0.2     0.0     \n",
      "66__34          5   6   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.23     0.01    0.24    0.0     \n",
      "66__34          6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.27     0.01    0.28    0.0     \n",
      "66__34          7   8   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.2      0.01    0.21    0.0     \n",
      "66__34          8   9   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.21    0.0     \n",
      "66__34          9   10  DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.21     0.0     0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "67__34          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "67__34          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.26     0.01    0.27    0.0     \n",
      "67__34          2   3   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.24     0.01    0.24    0.0     \n",
      "67__34          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.24     0.02    0.25    0.0     \n",
      "67__34          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.22     0.01    0.22    0.0     \n",
      "67__34          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "67__34          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "67__34          7   8   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.22     0.01    0.23    0.0     \n",
      "67__34          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.33     0.01    0.34    0.0     \n",
      "67__34          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "68__31          0   1   DT      0.68  0.66  0.73  0.66  0.6803   0.64  0.2      0.01    0.21    0.0     \n",
      "68__31          1   2   DT      0.68  0.66  0.73  0.66  0.6801   0.64  0.2      0.01    0.21    0.0     \n",
      "68__31          2   3   DT      0.68  0.66  0.73  0.66  0.6802   0.64  0.2      0.01    0.21    0.0     \n",
      "68__31          3   4   DT      0.68  0.66  0.73  0.66  0.6797   0.64  0.18     0.03    0.22    0.0     \n",
      "68__31          4   5   DT      0.68  0.66  0.73  0.66  0.6802   0.64  0.2      0.01    0.21    0.0     \n",
      "68__31          5   6   DT      0.68  0.66  0.73  0.66  0.6803   0.64  0.2      0.01    0.2     0.0     \n",
      "68__31          6   7   DT      0.68  0.66  0.73  0.66  0.6801   0.64  0.2      0.01    0.21    0.0     \n",
      "68__31          7   8   DT      0.68  0.66  0.73  0.66  0.6799   0.64  0.19     0.01    0.2     0.0     \n",
      "68__31          8   9   DT      0.68  0.66  0.73  0.66  0.6802   0.64  0.2      0.01    0.21    0.0     \n",
      "68__31          9   10  DT      0.68  0.66  0.73  0.66  0.6802   0.64  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "69__32          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "69__32          1   2   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.22    0.0     \n",
      "69__32          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.2     0.0     \n",
      "69__32          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.2     0.0     \n",
      "69__32          4   5   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.19     0.01    0.2     0.0     \n",
      "69__32          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "69__32          6   7   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.2      0.01    0.21    0.0     \n",
      "69__32          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.19    0.0     \n",
      "69__32          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "69__32          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "70__35          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "70__35          1   2   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.2     0.0     \n",
      "70__35          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "70__35          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.2     0.0     \n",
      "70__35          4   5   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.21     0.01    0.21    0.0     \n",
      "70__35          5   6   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.2     0.0     \n",
      "70__35          6   7   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.2     0.0     \n",
      "70__35          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "70__35          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.2     0.0     \n",
      "70__35          9   10  DT      0.7   0.7   0.76  0.7   0.721    0.67  0.2      0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "71__36          0   1   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.2     0.0     \n",
      "71__36          1   2   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "71__36          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "71__36          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.22    0.0     \n",
      "71__36          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "71__36          5   6   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.25     0.01    0.25    0.0     \n",
      "71__36          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.22     0.01    0.23    0.0     \n",
      "71__36          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71__36          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "71__36          9   10  DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "72__31          0   1   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.19     0.01    0.2     0.0     \n",
      "72__31          1   2   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "72__31          2   3   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.19     0.01    0.2     0.0     \n",
      "72__31          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.2     0.0     \n",
      "72__31          4   5   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "72__31          5   6   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.19     0.01    0.2     0.0     \n",
      "72__31          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "72__31          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "72__31          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.01    0.2     0.0     \n",
      "72__31          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "73__31          0   1   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.18     0.01    0.18    0.0     \n",
      "73__31          1   2   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.18     0.01    0.19    0.0     \n",
      "73__31          2   3   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.18     0.01    0.19    0.0     \n",
      "73__31          3   4   DT      0.7   0.7   0.76  0.7   0.7212   0.67  0.18     0.01    0.19    0.0     \n",
      "73__31          4   5   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.17     0.01    0.18    0.0     \n",
      "73__31          5   6   DT      0.7   0.7   0.76  0.7   0.7212   0.67  0.18     0.01    0.19    0.0     \n",
      "73__31          6   7   DT      0.7   0.7   0.76  0.7   0.721    0.67  0.18     0.01    0.19    0.0     \n",
      "73__31          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.67  0.18     0.01    0.19    0.0     \n",
      "73__31          8   9   DT      0.7   0.7   0.76  0.7   0.7213   0.67  0.17     0.01    0.18    0.0     \n",
      "73__31          9   10  DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.17     0.01    0.18    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "74__33          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "74__33          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.23     0.01    0.24    0.0     \n",
      "74__33          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "74__33          3   4   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.19     0.01    0.2     0.0     \n",
      "74__33          4   5   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.2     0.0     \n",
      "74__33          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "74__33          6   7   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.21     0.01    0.22    0.0     \n",
      "74__33          7   8   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.22     0.01    0.23    0.0     \n",
      "74__33          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "74__33          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "75__33          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "75__33          1   2   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.21    0.0     \n",
      "75__33          2   3   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "75__33          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "75__33          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "75__33          5   6   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "75__33          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "75__33          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "75__33          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "75__33          9   10  DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "76__37          0   1   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.0     0.2     0.0     \n",
      "76__37          1   2   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.02    0.21    0.0     \n",
      "76__37          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.0     0.21    0.0     \n",
      "76__37          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "76__37          4   5   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.01    0.21    0.0     \n",
      "76__37          5   6   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.21     0.01    0.21    0.0     \n",
      "76__37          6   7   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.21     0.01    0.21    0.0     \n",
      "76__37          7   8   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.01    0.21    0.0     \n",
      "76__37          8   9   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.2      0.01    0.21    0.0     \n",
      "76__37          9   10  DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "77__37          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.18     0.01    0.19    0.0     \n",
      "77__37          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "77__37          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.01    0.19    0.0     \n",
      "77__37          3   4   DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.2      0.01    0.21    0.0     \n",
      "77__37          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.22     0.01    0.23    0.0     \n",
      "77__37          5   6   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "77__37          6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.01    0.21    0.0     \n",
      "77__37          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "77__37          8   9   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.01    0.2     0.0     \n",
      "77__37          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "78__32          0   1   DT      0.7   0.7   0.76  0.7   0.7208   0.67  0.19     0.01    0.2     0.0     \n",
      "78__32          1   2   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.21     0.01    0.21    0.0     \n",
      "78__32          2   3   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.19     0.01    0.19    0.0     \n",
      "78__32          3   4   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.19     0.02    0.21    0.0     \n",
      "78__32          4   5   DT      0.7   0.7   0.76  0.7   0.7211   0.67  0.21     0.01    0.21    0.0     \n",
      "78__32          5   6   DT      0.7   0.7   0.76  0.7   0.7208   0.67  0.2      0.01    0.21    0.0     \n",
      "78__32          6   7   DT      0.7   0.7   0.76  0.7   0.7213   0.67  0.21     0.01    0.22    0.0     \n",
      "78__32          7   8   DT      0.7   0.7   0.76  0.7   0.7212   0.67  0.19     0.01    0.2     0.0     \n",
      "78__32          8   9   DT      0.7   0.7   0.76  0.7   0.721    0.67  0.19     0.01    0.2     0.0     \n",
      "78__32          9   10  DT      0.7   0.7   0.76  0.7   0.7213   0.67  0.22     0.01    0.22    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79__31          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.01    0.22    0.0     \n",
      "79__31          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "79__31          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "79__31          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "79__31          4   5   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.19     0.01    0.2     0.0     \n",
      "79__31          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "79__31          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.2     0.0     \n",
      "79__31          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "79__31          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "79__31          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "80__34          0   1   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.21     0.01    0.21    0.0     \n",
      "80__34          1   2   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.21     0.01    0.22    0.0     \n",
      "80__34          2   3   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.01    0.2     0.0     \n",
      "80__34          3   4   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.19     0.01    0.2     0.0     \n",
      "80__34          4   5   DT      0.7   0.7   0.76  0.7   0.721    0.68  0.2      0.01    0.21    0.0     \n",
      "80__34          5   6   DT      0.7   0.7   0.76  0.7   0.7208   0.68  0.2      0.01    0.2     0.0     \n",
      "80__34          6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.01    0.21    0.0     \n",
      "80__34          7   8   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.01    0.21    0.0     \n",
      "80__34          8   9   DT      0.7   0.7   0.76  0.7   0.721    0.68  0.23     0.01    0.24    0.0     \n",
      "80__34          9   10  DT      0.7   0.7   0.76  0.7   0.7208   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "81__29          0   1   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.18     0.01    0.18    0.0     \n",
      "81__29          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.17     0.01    0.18    0.0     \n",
      "81__29          2   3   DT      0.7   0.7   0.76  0.7   0.721    0.67  0.17     0.01    0.18    0.0     \n",
      "81__29          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.01    0.19    0.0     \n",
      "81__29          4   5   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.18     0.01    0.19    0.0     \n",
      "81__29          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.01    0.18    0.0     \n",
      "81__29          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.18     0.01    0.18    0.0     \n",
      "81__29          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.01    0.18    0.0     \n",
      "81__29          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.18     0.01    0.18    0.0     \n",
      "81__29          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.17     0.01    0.18    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "82__36          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "82__36          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "82__36          2   3   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.01    0.21    0.0     \n",
      "82__36          3   4   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.22     0.01    0.23    0.0     \n",
      "82__36          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "82__36          5   6   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "82__36          6   7   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "82__36          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.23     0.01    0.23    0.0     \n",
      "82__36          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "82__36          9   10  DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "83__33          0   1   DT      0.7   0.7   0.76  0.7   0.7196   0.68  0.21     0.01    0.21    0.0     \n",
      "83__33          1   2   DT      0.7   0.7   0.76  0.7   0.7196   0.68  0.2      0.01    0.21    0.0     \n",
      "83__33          2   3   DT      0.7   0.7   0.76  0.7   0.7195   0.68  0.22     0.01    0.23    0.0     \n",
      "83__33          3   4   DT      0.7   0.7   0.76  0.7   0.7196   0.68  0.21     0.01    0.21    0.0     \n",
      "83__33          4   5   DT      0.7   0.7   0.76  0.7   0.7196   0.68  0.21     0.02    0.23    0.0     \n",
      "83__33          5   6   DT      0.7   0.7   0.76  0.7   0.7195   0.68  0.21     0.01    0.22    0.0     \n",
      "83__33          6   7   DT      0.7   0.7   0.76  0.7   0.7195   0.68  0.22     0.0     0.22    0.0     \n",
      "83__33          7   8   DT      0.7   0.7   0.76  0.7   0.7195   0.68  0.2      0.0     0.2     0.0     \n",
      "83__33          8   9   DT      0.7   0.7   0.76  0.7   0.7194   0.68  0.2      0.01    0.21    0.0     \n",
      "83__33          9   10  DT      0.7   0.7   0.76  0.7   0.7195   0.68  0.2      0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "84__33          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "84__33          1   2   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.21    0.0     \n",
      "84__33          2   3   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "84__33          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.02    0.2     0.0     \n",
      "84__33          4   5   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.21     0.0     0.21    0.0     \n",
      "84__33          5   6   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.25     0.01    0.25    0.0     \n",
      "84__33          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.0     0.19    0.0     \n",
      "84__33          7   8   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "84__33          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n",
      "84__33          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "85__33          0   1   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.0     0.2     0.0     \n",
      "85__33          1   2   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.19     0.0     0.19    0.0     \n",
      "85__33          2   3   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.21    0.0     \n",
      "85__33          3   4   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "85__33          4   5   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.19     0.01    0.2     0.0     \n",
      "85__33          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.0     0.21    0.0     \n",
      "85__33          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "85__33          7   8   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "85__33          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "85__33          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.0     0.19    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "86__32          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "86__32          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86__32          2   3   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.0     0.2     0.0     \n",
      "86__32          3   4   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.21     0.0     0.21    0.0     \n",
      "86__32          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.18     0.02    0.2     0.0     \n",
      "86__32          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "86__32          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.0     0.19    0.0     \n",
      "86__32          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "86__32          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n",
      "86__32          9   10  DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.19     0.0     0.19    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "87__31          0   1   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.18     0.01    0.19    0.0     \n",
      "87__31          1   2   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.18     0.01    0.19    0.0     \n",
      "87__31          2   3   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.19     0.01    0.2     0.0     \n",
      "87__31          3   4   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.18     0.02    0.19    0.0     \n",
      "87__31          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.17     0.02    0.19    0.0     \n",
      "87__31          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "87__31          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.18     0.01    0.19    0.0     \n",
      "87__31          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n",
      "87__31          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.01    0.19    0.0     \n",
      "87__31          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "88__33          0   1   DT      0.7   0.7   0.76  0.7   0.7191   0.68  0.2      0.0     0.2     0.0     \n",
      "88__33          1   2   DT      0.7   0.7   0.76  0.7   0.7196   0.68  0.2      0.0     0.2     0.0     \n",
      "88__33          2   3   DT      0.7   0.7   0.76  0.7   0.7194   0.68  0.21     0.01    0.22    0.0     \n",
      "88__33          3   4   DT      0.7   0.7   0.76  0.7   0.7191   0.68  0.2      0.02    0.22    0.0     \n",
      "88__33          4   5   DT      0.7   0.7   0.76  0.7   0.7195   0.68  0.19     0.01    0.2     0.0     \n",
      "88__33          5   6   DT      0.7   0.7   0.76  0.7   0.7195   0.68  0.2      0.01    0.21    0.0     \n",
      "88__33          6   7   DT      0.7   0.7   0.76  0.7   0.7192   0.68  0.2      0.02    0.22    0.0     \n",
      "88__33          7   8   DT      0.7   0.7   0.76  0.7   0.7192   0.68  0.19     0.01    0.2     0.0     \n",
      "88__33          8   9   DT      0.7   0.7   0.76  0.7   0.7197   0.68  0.21     0.01    0.21    0.0     \n",
      "88__33          9   10  DT      0.7   0.7   0.76  0.7   0.7196   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "89__34          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.22    0.0     \n",
      "89__34          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "89__34          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "89__34          3   4   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.0     0.2     0.0     \n",
      "89__34          4   5   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.21    0.0     \n",
      "89__34          5   6   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.21     0.0     0.22    0.0     \n",
      "89__34          6   7   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.25     0.02    0.27    0.0     \n",
      "89__34          7   8   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.22     0.0     0.22    0.0     \n",
      "89__34          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.22     0.0     0.22    0.0     \n",
      "89__34          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.01    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "90__34          0   1   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.0     0.2     0.0     \n",
      "90__34          1   2   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.19     0.01    0.2     0.0     \n",
      "90__34          2   3   DT      0.7   0.7   0.76  0.7   0.7214   0.67  0.19     0.01    0.2     0.0     \n",
      "90__34          3   4   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "90__34          4   5   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.0     0.21    0.0     \n",
      "90__34          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "90__34          6   7   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.19     0.01    0.2     0.0     \n",
      "90__34          7   8   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.19     0.0     0.19    0.0     \n",
      "90__34          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "90__34          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "91__31          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "91__31          1   2   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.0     0.2     0.0     \n",
      "91__31          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "91__31          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "91__31          4   5   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.19     0.01    0.2     0.0     \n",
      "91__31          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "91__31          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.0     0.19    0.0     \n",
      "91__31          7   8   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.19     0.0     0.19    0.0     \n",
      "91__31          8   9   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.0     0.2     0.0     \n",
      "91__31          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.18     0.02    0.19    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "92__34          0   1   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.2      0.01    0.21    0.0     \n",
      "92__34          1   2   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.22     0.01    0.22    0.0     \n",
      "92__34          2   3   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "92__34          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "92__34          4   5   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "92__34          5   6   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.21    0.0     \n",
      "92__34          6   7   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.2      0.01    0.21    0.0     \n",
      "92__34          7   8   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.02    0.22    0.0     \n",
      "92__34          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "92__34          9   10  DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.02    0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "93__35          0   1   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.01    0.2     0.0     \n",
      "93__35          1   2   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "93__35          2   3   DT      0.7   0.7   0.76  0.7   0.7211   0.68  0.2      0.02    0.22    0.0     \n",
      "93__35          3   4   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.01    0.21    0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93__35          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.18     0.02    0.2     0.0     \n",
      "93__35          5   6   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "93__35          6   7   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "93__35          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n",
      "93__35          8   9   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.19     0.01    0.2     0.0     \n",
      "93__35          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.21     0.0     0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "94__33          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "94__33          1   2   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.01    0.21    0.0     \n",
      "94__33          2   3   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.21    0.0     \n",
      "94__33          3   4   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.0     0.2     0.0     \n",
      "94__33          4   5   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n",
      "94__33          5   6   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.21    0.0     \n",
      "94__33          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.0     0.21    0.0     \n",
      "94__33          7   8   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.22     0.01    0.22    0.0     \n",
      "94__33          8   9   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.01    0.2     0.0     \n",
      "94__33          9   10  DT      0.7   0.7   0.76  0.7   0.7216   0.67  0.2      0.0     0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "95__35          0   1   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.2      0.0     0.2     0.0     \n",
      "95__35          1   2   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.21     0.0     0.21    0.0     \n",
      "95__35          2   3   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.02    0.22    0.0     \n",
      "95__35          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "95__35          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.01    0.2     0.0     \n",
      "95__35          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "95__35          6   7   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.22    0.0     \n",
      "95__35          7   8   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.0     0.2     0.0     \n",
      "95__35          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "95__35          9   10  DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.21     0.0     0.21    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "96__31          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.18     0.01    0.19    0.0     \n",
      "96__31          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n",
      "96__31          2   3   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "96__31          3   4   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.0     0.2     0.0     \n",
      "96__31          4   5   DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.18     0.01    0.19    0.0     \n",
      "96__31          5   6   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.2      0.01    0.2     0.0     \n",
      "96__31          6   7   DT      0.7   0.7   0.76  0.7   0.7215   0.67  0.18     0.02    0.2     0.0     \n",
      "96__31          7   8   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.19     0.01    0.2     0.0     \n",
      "96__31          8   9   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.01    0.2     0.0     \n",
      "96__31          9   10  DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.0     0.19    0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "97__33          0   1   DT      0.7   0.7   0.76  0.7   0.7191   0.68  0.2      0.02    0.22    0.0     \n",
      "97__33          1   2   DT      0.7   0.7   0.76  0.7   0.7194   0.68  0.2      0.02    0.22    0.0     \n",
      "97__33          2   3   DT      0.7   0.7   0.76  0.7   0.7195   0.68  0.21     0.0     0.21    0.0     \n",
      "97__33          3   4   DT      0.7   0.7   0.76  0.7   0.7196   0.68  0.21     0.01    0.21    0.0     \n",
      "97__33          4   5   DT      0.7   0.7   0.76  0.7   0.7194   0.68  0.21     0.01    0.21    0.0     \n",
      "97__33          5   6   DT      0.7   0.7   0.76  0.7   0.7195   0.68  0.2      0.01    0.21    0.0     \n",
      "97__33          6   7   DT      0.7   0.7   0.76  0.7   0.7195   0.68  0.22     0.01    0.23    0.0     \n",
      "97__33          7   8   DT      0.7   0.7   0.76  0.7   0.7194   0.68  0.2      0.0     0.2     0.0     \n",
      "97__33          8   9   DT      0.7   0.7   0.76  0.7   0.7195   0.68  0.2      0.0     0.21    0.0     \n",
      "97__33          9   10  DT      0.7   0.7   0.76  0.7   0.7195   0.68  0.2      0.0     0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "98__32          0   1   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.18     0.02    0.19    0.0     \n",
      "98__32          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.02    0.2     0.0     \n",
      "98__32          2   3   DT      0.7   0.7   0.76  0.7   0.7214   0.68  0.18     0.01    0.19    0.0     \n",
      "98__32          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "98__32          4   5   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.01    0.2     0.0     \n",
      "98__32          5   6   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.2      0.02    0.22    0.0     \n",
      "98__32          6   7   DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.01    0.21    0.0     \n",
      "98__32          7   8   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.19     0.0     0.19    0.0     \n",
      "98__32          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.19     0.01    0.2     0.0     \n",
      "98__32          9   10  DT      0.7   0.7   0.76  0.7   0.7212   0.68  0.19     0.01    0.2     0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "99__34          0   1   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "99__34          1   2   DT      0.7   0.7   0.76  0.7   0.7216   0.68  0.21     0.01    0.21    0.0     \n",
      "99__34          2   3   DT      0.7   0.7   0.76  0.7   0.7213   0.68  0.18     0.02    0.2     0.0     \n",
      "99__34          3   4   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.01    0.21    0.0     \n",
      "99__34          4   5   DT      0.7   0.7   0.76  0.7   0.7215   0.68  0.2      0.01    0.21    0.0     \n",
      "99__34          5   6   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.2     0.0     \n",
      "99__34          6   7   DT      0.7   0.7   0.76  0.7   0.7218   0.68  0.19     0.02    0.21    0.0     \n",
      "99__34          7   8   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.03    0.23    0.0     \n",
      "99__34          8   9   DT      0.7   0.7   0.76  0.7   0.7217   0.68  0.2      0.0     0.21    0.0     \n",
      "99__34          9   10  DT      0.7   0.7   0.76  0.7   0.7219   0.68  0.2      0.0     0.2     0.0     \n"
     ]
    }
   ],
   "source": [
    "test='./Aalto_test_IoTDevID.csv'\n",
    "train='./Aalto_train_IoTDevID.csv'\n",
    "sayac=1\n",
    "features=sonuç\n",
    "#features=[['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'ICMP_type', 'TCP_flags', 'TCP_FIN', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'Protocol', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_type', 'IP_version', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_flags', 'TCP_SYN', 'TCP_PSH', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_qdcount', 'DNS_nscount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'ICMP_type', 'TCP_flags', 'TCP_FIN', 'TCP_window', 'TCP_options', 'UDP_len', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'EAPOL_version', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_len', 'IP_tos', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_file', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_version', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen',\n",
    "#'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_arcount', 'sport_class', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_DF', 'IP_ttl', 'IP_proto', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_rd', 'DNS_rcode', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_DF', 'IP_ttl', 'IP_proto', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_PSH', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_DF', 'IP_ttl', 'IP_proto', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_ihl', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_file', 'DNS_qr', 'DNS_rd', 'DNS_rcode', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'IP_ihl', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_file', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_file', 'DNS_qr', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'Ether_type', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_flags', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_DF', 'IP_ttl', 'IP_proto', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_ihl', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'DNS_qr', 'DNS_rd', 'DNS_rcode', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'Ether_type', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_DF', 'IP_ttl', 'ICMP_type', 'ICMP_seq', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_rd', 'DNS_ra', 'DNS_rcode', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_tos', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_flags', 'TCP_SYN', 'TCP_ACK', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'DNS_qr', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_file', 'DNS_qr', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_PSH', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_flags', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_flags', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl',\n",
    "#'EAPOL_version', 'EAPOL_len', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_file', 'DNS_qr', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_PSH', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_hlen', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_len', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_options', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_ancount', 'DNS_nscount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_DF', 'IP_ttl', 'IP_proto', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_aa', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'LLC_ssap', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_version', 'IP_ihl', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'ICMP_type', 'ICMP_code', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_PSH', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_htype', 'BOOTP_sname', 'BOOTP_file', 'DNS_qr', 'DNS_aa', 'DNS_rd', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label'], ['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'EAPOL_len', 'IP_tos', 'IP_flags', 'IP_ttl', 'IP_proto', 'IP_options', 'ICMP_type', 'ICMP_seq', 'TCP_dataofs', 'TCP_flags', 'TCP_FIN', 'TCP_SYN', 'TCP_RST', 'TCP_ACK', 'TCP_window', 'DHCP_options', 'BOOTP_op', 'BOOTP_hlen', 'BOOTP_secs', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_ra', 'DNS_rcode', 'DNS_qdcount', 'DNS_ancount', 'DNS_arcount', 'dport_class', 'payload_bytes', 'entropy', 'Protocol', 'Label']]\n",
    "\n",
    "step=1\n",
    "mixed=False\n",
    "\n",
    "ml_list={\"DT\" :DecisionTreeClassifier()}\n",
    "cm=False\n",
    "repetition=10\n",
    "\n",
    "\n",
    "folder(\"./100/\")\n",
    "\n",
    "\n",
    "\n",
    "for sayac,feature in enumerate(features):\n",
    "    output_csv=\"./100/\"+str(sayac)+\"_\"+str(len(feature))+\".csv\"\n",
    "    dataset=str(sayac)+\"__\"+str(len(feature))\n",
    "    feature.insert(0,\"MAC\")\n",
    "    ML(train,test,output_csv,feature,step,mixed,dataset,ml_list,cm,repetition)  \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# these  result list taken from GA\n",
    "all_features_score_aalto=0.7003946487780672                                       \n",
    "mean_of_gean_aalto=[\n",
    "0.658976323,\n",
    "0.690342384,\n",
    "0.70377347,\n",
    "0.710583201,\n",
    "0.715113088,\n",
    "0.716330202,\n",
    "0.715825834,\n",
    "0.717101207,\n",
    "0.714730845,\n",
    "0.717147627,\n",
    "0.719827763,\n",
    "0.71687345,\n",
    "0.719555657,\n",
    "0.71784551,\n",
    "0.720511176,\n",
    "0.71761989,\n",
    "0.7208948,\n",
    "0.719915251,\n",
    "0.719071419,\n",
    "0.717713946,\n",
    "0.71852689,\n",
    "0.719481632,\n",
    "0.720086608,\n",
    "0.720659253,\n",
    "0.717996341,\n",
    "0.720695689,\n",
    "0.719423281,\n",
    "0.719209018,\n",
    "0.720199103,\n",
    "0.719667945,\n",
    "0.719187511,\n",
    "0.720041875,\n",
    "0.71853992,\n",
    "0.720302949,\n",
    "0.718784064,\n",
    "0.720059881,\n",
    "0.720123043,\n",
    "0.717933308,\n",
    "0.719474679,\n",
    "0.720819512,\n",
    "0.720453079,\n",
    "0.719996107,\n",
    "0.719966414,\n",
    "0.717600793,\n",
    "0.71963478,\n",
    "0.720071005,\n",
    "0.719987193,\n",
    "0.720115493,\n",
    "0.719696026,\n",
    "0.721087154,\n",
    "0.717968351,\n",
    "0.719526983,\n",
    "0.720521227,\n",
    "0.719682923,\n",
    "0.718161014,\n",
    "0.71961584,\n",
    "0.720972553,\n",
    "0.71862179,\n",
    "0.718258233,\n",
    "0.718730554,\n",
    "0.719514412,\n",
    "0.720331836,\n",
    "0.720606497,\n",
    "0.720477354,\n",
    "0.720010626,\n",
    "0.717763388,\n",
    "0.718967923,\n",
    "0.718510293,\n",
    "0.720149418,\n",
    "0.719662958,\n",
    "0.719015127,\n",
    "0.719849898,\n",
    "0.718018871,\n",
    "0.721156234,\n",
    "0.71992728,\n",
    "0.719043396,\n",
    "0.720337345,\n",
    "0.719655518,\n",
    "0.719187358,\n",
    "0.720663234,\n",
    "0.72010119,\n",
    "0.720624521,\n",
    "0.720547255,\n",
    "0.718880147,\n",
    "0.718824711,\n",
    "0.7192739,\n",
    "0.7199886,\n",
    "0.719052036,\n",
    "0.720159135,\n",
    "0.717845337,\n",
    "0.720058593,\n",
    "0.720912641,\n",
    "0.71953248,\n",
    "0.720038212,\n",
    "0.720384589,\n",
    "0.72093063,\n",
    "0.719072245,\n",
    "0.720627628,\n",
    "0.71839229,\n",
    "0.718327975]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_list=find_the_way('./100/','.csv')\n",
    "name_list\n",
    "\n",
    "flag=1\n",
    "for i in name_list:\n",
    "    df = pd.read_csv(i) \n",
    "    col=i[6:-4]\n",
    "    temp=pd.DataFrame(df.mean(numeric_only=True),columns=[col])\n",
    "    if flag:\n",
    "        std=temp\n",
    "        flag=0\n",
    "    else:\n",
    "        std[col]=temp[col]\n",
    "        \n",
    "        \n",
    "aalto=std.T\n",
    "aalto=aalto.sort_values(by=[' F1-score'])\n",
    "aalto\n",
    "\n",
    "aalto.to_csv(\"mean_100.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_scores_aalto=[all_features_score_aalto]\n",
    "best_score_aalto=all_features_score_aalto\n",
    "for i in aalto[' F1-score']:\n",
    "    if i > best_score_aalto:\n",
    "        best_score_aalto=i\n",
    "    best_scores_aalto.append(best_score_aalto)\n",
    "    \n",
    "best_scores_aalto=best_scores_aalto[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "graph_name=\"100feature selection merge using genetic algorithm.pdf\"\n",
    "my_xticks=list(range(len(aalto)))\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':'x-large',\n",
    "         'ytick.labelsize':'x-large'}\n",
    "pylab.rcParams.update(params)\n",
    "#plt.figure(figsize=(10,10))\n",
    "#plt.plot(my_xticks,tt['Acc'], linestyle='--', marker='.', color='b',label= \"Separate Train & Test acc\")\n",
    "#plt.plot(my_xticks,cv['Acc'], linestyle='--', marker='.', color='r',label= \"10-Fold CV acc\")\n",
    "\n",
    "\n",
    "plt.plot(my_xticks,aalto[' F1-score'], linestyle='', marker='.', color='b',label= \"Generation Average Score\")\n",
    "plt.plot(my_xticks,best_scores_aalto, linestyle='-', marker='', color='g',label= \"Best Score\")\n",
    "\n",
    "#plt.plot(my_xticks,mean_of_gean, linestyle='-', marker='', color='r',label= \"Best Score\")\n",
    "#plt.plot(my_xticks,cv[' F1-score'], linestyle='-', marker='o', color='b',label= \"10-Fold CV F1\") \n",
    "plt.axhline(all_features_score_aalto   , color='r', label= \"All Features Score\")## hepsi\n",
    "\n",
    "#plt.axhline(0.7382699395282875 , color='g',  label= \"F1 score using all features\")\n",
    "plt.title(\"Feature Selection Process Using GA in Aalto Dataset\")\n",
    "plt.legend(numpoints=1)\n",
    "#plt.annotate(s='', xy=(89,0.70), xytext=(89,0.72), arrowprops=dict(arrowstyle='->'))\n",
    "#plt.arrow(89,0.70, 89,0.72, head_width=0.1)\n",
    "#plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "#plt.arrow(x=89, y=0.650, dx=0, dy=0.11, width=.1)\n",
    "plt.ylabel(\"F1 Score\")\n",
    "plt.xlabel(\"Generation Number\")\n",
    "plt.xticks(rotation=90) \n",
    "plt.ylim([0.69, 0.7250]) \n",
    "plt.savefig(graph_name,bbox_inches='tight',format=\"pdf\")#, dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "chosen_Feature_set=['pck_size', 'Ether_type', 'LLC_ctrl', 'EAPOL_version', 'EAPOL_type', 'IP_ihl', 'IP_tos', 'IP_len', 'IP_flags', 'IP_DF', 'IP_ttl', 'IP_options', 'ICMP_code', 'TCP_dataofs', 'TCP_FIN', 'TCP_ACK', 'TCP_window', 'UDP_len', 'DHCP_options', 'BOOTP_hlen', 'BOOTP_flags', 'BOOTP_sname', 'BOOTP_file', 'BOOTP_options', 'DNS_qr', 'DNS_rd', 'DNS_qdcount', 'dport_class', 'payload_bytes', 'entropy', 'Label']"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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