0004 Performance Evaluation/04.4 Aalto results with combined labels.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  importing relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from numpy import array\n",
    "from random import random\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import BernoulliNB#57\n",
    "from sklearn.naive_bayes import GaussianNB#52\n",
    "from sklearn.naive_bayes import MultinomialNB#56\n",
    "from sklearn.naive_bayes import CategoricalNB\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.utils import shuffle\n",
    "import csv\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import time\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discovering Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def target_name(name):\n",
    "    df = pd.read_csv(name)\n",
    "    df=df.replace({\"Label\": new_labels})\n",
    "    target_names=sorted(list(df[df.columns[-1]].unique()))\n",
    "    return target_names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def folder(f_name): #this function creates a folder.\n",
    "    try:\n",
    "        if not os.path.exists(f_name):\n",
    "            os.makedirs(f_name)\n",
    "    except OSError:\n",
    "        print (\"Tthe folder could not be created!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": "markdown",
   "metadata": {},
   "source": [
    "# Aggregation Algorithm notmal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "altime=0\n",
    "#def most_frequent(List): \n",
    "#    return max(set(List), key = List.count) \n",
    "\n",
    "\n",
    " \n",
    "def most_frequent(List):\n",
    "    occurence_count = Counter(List)\n",
    "    occurence_count={k: v for k, v in sorted(occurence_count.items(), key=lambda item: item[1],reverse=True)}\n",
    "    big=list(occurence_count.values())\n",
    "    big=big.count(big[0])\n",
    "    return list(occurence_count.keys())[np.random.randint(big)]\n",
    "\n",
    "\n",
    "def split(a, n):\n",
    "    k, m = divmod(len(a), n)\n",
    "    return (a[i * k + min(i, m):(i + 1) * k + min(i + 1, m)] for i in range(n))\n",
    "def create_exception(df): \n",
    "    exception_list=[]\n",
    "    dominant_mac=[]\n",
    "    for i in df['aggregated'].unique():\n",
    "        k=df[df['aggregated']==i]\n",
    "        for ii in ['MAC']:\n",
    "            hist = {}\n",
    "            for x in k[ii].values:\n",
    "                hist[x] = hist.get(x, 0) + 1\n",
    "            hist=dict(sorted(hist.items(), key=lambda item: item[1],reverse=True))\n",
    "            temp=next(iter(hist))\n",
    "            if temp not in dominant_mac:\n",
    "                dominant_mac.append(temp)\n",
    "            else:\n",
    "                exception_list.append(temp)\n",
    "    return exception_list\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def merged(m_test,predict,step,mixed):\n",
    "    second=time.time()\n",
    "    mac_test=[]\n",
    "    for q in m_test.index:\n",
    "        mac_test.append(m_test[q])\n",
    "\n",
    "    d_list=sorted(list(m_test.unique()))\n",
    "    devices={}\n",
    "    for q in d_list:\n",
    "        devices[q]=[]    \n",
    "\n",
    "\n",
    "    new_y=[0]*len(m_test)\n",
    "\n",
    "    for q,qq in enumerate (mac_test):\n",
    "        devices[qq].append(q)\n",
    "    for q in devices:\n",
    "        a = [devices[q][j:j + step] for j in range(0, len(devices[q]), step)]  \n",
    "        for qq in a:\n",
    "            step_list=[]\n",
    "            for qqq in qq:\n",
    "                step_list.append(predict[qqq])\n",
    "            add=most_frequent(list(step_list))\n",
    "            for qqq in qq:\n",
    "                new_y[qqq]=add\n",
    "    results=pd.DataFrame(m_test)\n",
    "    results[\"aggregated\"]=new_y\n",
    "    results[\"normal\"]=predict\n",
    "    \n",
    "    #MIXED METHOD\n",
    "    if mixed:\n",
    "        exception=create_exception(results)\n",
    "        for q in exception:\n",
    "            results.loc[results.MAC == q, 'aggregated'] = results['normal']\n",
    "\n",
    "    return results[\"aggregated\"].values,time.time()-second\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculation of evaluations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def score(altime,train_time,test_time,predict,y_test,class_based_results,i,cv,dname,ii):\n",
    "    precision=[]\n",
    "    recall=[]\n",
    "    f1=[]\n",
    "    accuracy=[]\n",
    "    total_time=[]\n",
    "    kappa=[]\n",
    "    accuracy_b=[]\n",
    "    \n",
    "    rc=sklearn.metrics.recall_score(y_test, predict,average= \"macro\")\n",
    "    pr=sklearn.metrics.precision_score(y_test, predict,average= \"macro\")\n",
    "    f_1=sklearn.metrics.f1_score(y_test, predict,average= \"macro\")        \n",
    "    report = classification_report(y_test, predict, target_names=target_names,output_dict=True)\n",
    "    cr = pd.DataFrame(report).transpose()\n",
    "    if class_based_results.empty:\n",
    "        class_based_results =cr\n",
    "    else:\n",
    "        class_based_results = class_based_results.add(cr, fill_value=0)\n",
    "    precision.append(float(pr))\n",
    "    recall.append(float(rc))\n",
    "    f1.append(float(f_1))\n",
    "    accuracy_b.append(balanced_accuracy_score( y_test,predict))\n",
    "    accuracy.append(accuracy_score(y_test, predict))\n",
    "\n",
    "    kappa.append(round(float(sklearn.metrics.cohen_kappa_score(y_test, predict, \n",
    "    labels=None, weights=None, sample_weight=None)),15))\n",
    "    print ('%-15s %-3s %-3s %-6s  %-5s %-5s %-5s %-5s %-8s %-5s %-8s %-8s%-8s%-8s' % (dname,i,cv,ii[0:6],str(round(np.mean(accuracy),2)),str(round(np.mean(accuracy_b),2)),\n",
    "        str(round(np.mean(precision),2)), str(round(np.mean(recall),2)),str(round(np.mean(f1),4)), \n",
    "        str(round(np.mean(kappa),2)),str(round(np.mean(train_time),2)),str(round(np.mean(test_time),2)),str(round(np.mean(test_time)+np.mean(train_time),2)),str(round(np.mean(altime),2))))\n",
    "    lines=(str(dname)+\",\"+str(i)+\",\"+str(cv)+\",\"+str(ii)+\",\"+str(round(np.mean(accuracy),15))+\",\"+str(round(np.mean(accuracy_b),15))+\",\"+str(round(np.mean(precision),15))+\",\"+ str(round(np.mean(recall),15))+\",\"+str(round(np.mean(f1),15))+\",\"+str(round(np.mean(kappa),15))+\",\"+str(round(np.mean(train_time),15))+\",\"+str(round(np.mean(test_time),15))+\",\"+str(altime)+\"\\n\")\n",
    "    return lines,class_based_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "import graphviz\n",
    "from graphviz import render\n",
    "def ciz(name,model,feature_names,target_names):\n",
    "\n",
    "    dot_data = tree.export_graphviz(model, out_file=None, \n",
    "                                feature_names=feature_names,  \n",
    "                                class_names=target_names,\n",
    "                                filled=True)\n",
    "\n",
    "    # Draw graph\n",
    "    #graph = graphviz.Source(dot_data) \n",
    "    graph = graphviz.Source(dot_data,format='pdf')    \n",
    "    name=name[:-4]\n",
    "    graph.render(name, view=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ML(loop1,loop2,output_csv,cols,step,mixed,dname):\n",
    "    maxy=0\n",
    "    ths = open(output_csv, \"w\")\n",
    "    ths.write(\"Dataset,T,CV,ML algorithm,Acc,b_Acc,Precision, Recall , F1-score, kappa ,tra-Time,test-Time,Al-Time\\n\")\n",
    "    \n",
    "\n",
    "    from sklearn.metrics import balanced_accuracy_score\n",
    "    from sklearn.preprocessing import Normalizer\n",
    "    \n",
    "    for ii in ml_list:\n",
    "        print ('%-15s %-3s %-3s %-6s  %-5s %-5s %-5s %-5s %-8s %-5s %-8s %-8s%-8s%-8s'%\n",
    "               (\"Dataset\",\"T\",\"CV\",\"ML alg\",\"Acc\",\"b_Acc\",\"Prec\", \"Rec\" , \"F1\", \"kap\" ,\"tra-T\",\"test-T\",\"total\",\"al-time\"))\n",
    "        class_based_results=pd.DataFrame()#\"\" #pd.DataFrame(0, index=np.arange((len(target_names)+3)), columns=[\"f1-score\",\"precision\",\"recall\",\"support\"])\n",
    "        cm=pd.DataFrame()\n",
    "        cv=0\n",
    "        if ii in [\"GB\",\"SVM\"]: #for slow algorithms.\n",
    "            repetition=10 \n",
    "        else:\n",
    "            repetition=100\n",
    "        for i in range(repetition):\n",
    "\n",
    "\n",
    "\n",
    "            #TRAIN\n",
    "            df = pd.read_csv(loop1,usecols=cols)\n",
    "            df=df.replace({\"Label\": new_labels})\n",
    "            m_train=df[\"MAC\"]\n",
    "            del df[\"MAC\"]\n",
    "            feature_names=df.columns\n",
    "            feature_names=feature_names[0:-1]\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",
    "            #TEST\n",
    "            df = pd.read_csv(loop2,usecols=cols)\n",
    "            df=df.replace({\"Label\": new_labels})\n",
    "            df = shuffle(df, random_state=42)\n",
    "            m_test=df[\"MAC\"]\n",
    "            del df[\"MAC\"]\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",
    "\n",
    "\n",
    "            results_y=[]\n",
    "            cv+=1\n",
    "            results_y.append(y_test)\n",
    "\n",
    "\n",
    "     \n",
    "   \n",
    "\n",
    "            #machine learning algorithm is applied in this section\n",
    "            clf = ml_list[ii]#choose algorithm from ml_list dictionary\n",
    "            second=time.time()\n",
    "            clf.fit(X_train, y_train)\n",
    "            train_time=(float((time.time()-second)) )\n",
    "            second=time.time()\n",
    "            predict =clf.predict(X_test)\n",
    "            test_time=(float((time.time()-second)) )\n",
    "            if step==1:\n",
    "                altime=0\n",
    "                lines,class_based_results=score(altime,train_time,test_time,predict,y_test,class_based_results,i,cv,dname,ii)\n",
    "            else:\n",
    "                predict,altime=merged(m_test,predict,step,mixed)\n",
    "                lines,class_based_results=score(altime,train_time,test_time,predict,y_test,class_based_results,i,cv,dname,ii)\n",
    "            ths.write (lines)\n",
    "            f1=sklearn.metrics.f1_score(y_test, predict,average= \"macro\")   \n",
    "            if maxy<f1:\n",
    "                maxy=f1\n",
    "                chosen=clf\n",
    "            \n",
    "\n",
    "                \n",
    "\n",
    "         \n",
    "\n",
    "            df_cm = pd.DataFrame(confusion_matrix(y_test, predict))\n",
    "            if cm.empty:\n",
    "                cm =df_cm\n",
    "            else:\n",
    "                cm = cm.add(df_cm, fill_value=0)\n",
    "            \n",
    "        class_based_results=class_based_results/repetition\n",
    "        print(class_based_results)\n",
    "        class_based_results.to_csv(\"class_based_results.csv\")\n",
    "        if True:\n",
    "            cm=cm//repetition\n",
    "            cm.to_csv(\"cm_13.csv\")\n",
    "            graph_name=output_csv+ii+\"_confusion matrix.pdf\"   \n",
    "            plt.figure(figsize = (10,7))\n",
    "            sns.heatmap(cm,xticklabels=target_names, yticklabels=target_names, annot=True, fmt='g')\n",
    "            plt.savefig(graph_name,bbox_inches='tight')#, dpi=400)\n",
    "            plt.show()\n",
    "            #print(cm)\n",
    "            print(\"\\n\\n\\n\")             \n",
    "\n",
    "\n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "\n",
    "\n",
    "    ths.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine learning applications "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aalto Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature= ['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',\n",
    "\"MAC\",\n",
    "          'Label']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_list={\"DT\" :DecisionTreeClassifier()}\n",
    "dataset=\"./Aalto combined labels/\"\n",
    "folder(dataset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_labels={ 'D-LinkSensor':\"D-LinkSensors\",\n",
    " 'D-LinkSiren':\"D-LinkSensors\",\n",
    " 'D-LinkSwitch':\"D-LinkSensors\",\n",
    " 'D-LinkWaterSensor':\"D-LinkSensors\",\n",
    " 'EdimaxPlug1101W':\"Edimax\",\n",
    " 'EdimaxPlug2101W':\"Edimax\",\n",
    " 'TP-LinkPlugHS100':\"TP-LinkPlugHS\",\n",
    " 'TP-LinkPlugHS110':\"TP-LinkPlugHS\",\n",
    "\"HueBridge\":'Hue-Device',\n",
    "\"HueSwitch\":'Hue-Device', \n",
    " 'WeMoLink':\"WeMos\",\n",
    " 'WeMoSwitch':\"WeMos\",  'WeMoInsightSwitch':\"WeMos\"      }\n",
    "\n",
    "\n",
    "\n",
    "def target_name(name):\n",
    "    df = pd.read_csv(name)\n",
    "    df=df.replace({\"Label\": new_labels})\n",
    "    target_names=sorted(list(df[df.columns[-1]].unique()))\n",
    "    return target_names\n",
    "\n",
    "\n",
    "def target_name(name):\n",
    "    df = pd.read_csv(name)\n",
    "    df=df.replace({\"Label\": new_labels})\n",
    "    target_names=sorted(list(df[df.columns[-1]].unique()))\n",
    "    return target_names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
      "Aalto/False_1   0   1   DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.0     0.15    0.0     \n",
      "Aalto/False_1   1   2   DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.0     0.16    0.0     \n",
      "Aalto/False_1   2   3   DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.17     0.01    0.17    0.0     \n",
      "Aalto/False_1   3   4   DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.18     0.01    0.19    0.0     \n",
      "Aalto/False_1   4   5   DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.01    0.16    0.0     \n",
      "Aalto/False_1   5   6   DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.16     0.01    0.17    0.0     \n",
      "Aalto/False_1   6   7   DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.16     0.0     0.16    0.0     \n",
      "Aalto/False_1   7   8   DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.16     0.01    0.17    0.0     \n",
      "Aalto/False_1   8   9   DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.15     0.0     0.16    0.0     \n",
      "Aalto/False_1   9   10  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.14     0.0     0.15    0.0     \n",
      "Aalto/False_1   10  11  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.15     0.01    0.15    0.0     \n",
      "Aalto/False_1   11  12  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.16     0.0     0.16    0.0     \n",
      "Aalto/False_1   12  13  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.16     0.0     0.16    0.0     \n",
      "Aalto/False_1   13  14  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.16     0.01    0.17    0.0     \n",
      "Aalto/False_1   14  15  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.17     0.01    0.17    0.0     \n",
      "Aalto/False_1   15  16  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   16  17  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   17  18  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.0     0.15    0.0     \n",
      "Aalto/False_1   18  19  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   19  20  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.0     0.15    0.0     \n",
      "Aalto/False_1   20  21  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   21  22  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.0     0.15    0.0     \n",
      "Aalto/False_1   22  23  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   23  24  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.0     0.15    0.0     \n",
      "Aalto/False_1   24  25  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   25  26  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   26  27  DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   27  28  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.0     0.15    0.0     \n",
      "Aalto/False_1   28  29  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   29  30  DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.15     0.01    0.16    0.0     \n",
      "Aalto/False_1   30  31  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.0     0.16    0.0     \n",
      "Aalto/False_1   31  32  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.17     0.0     0.18    0.0     \n",
      "Aalto/False_1   32  33  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.18     0.01    0.18    0.0     \n",
      "Aalto/False_1   33  34  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.0     0.15    0.0     \n",
      "Aalto/False_1   34  35  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.14     0.0     0.15    0.0     \n",
      "Aalto/False_1   35  36  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.19     0.01    0.19    0.0     \n",
      "Aalto/False_1   36  37  DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.15     0.01    0.16    0.0     \n",
      "Aalto/False_1   37  38  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.17     0.01    0.18    0.0     \n",
      "Aalto/False_1   38  39  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.17     0.01    0.18    0.0     \n",
      "Aalto/False_1   39  40  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.16     0.01    0.16    0.0     \n",
      "Aalto/False_1   40  41  DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   41  42  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.0     0.15    0.0     \n",
      "Aalto/False_1   42  43  DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.15     0.0     0.16    0.0     \n",
      "Aalto/False_1   43  44  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.22     0.01    0.23    0.0     \n",
      "Aalto/False_1   44  45  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.0     0.16    0.0     \n",
      "Aalto/False_1   45  46  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.16     0.01    0.17    0.0     \n",
      "Aalto/False_1   46  47  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.18     0.01    0.19    0.0     \n",
      "Aalto/False_1   47  48  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.16     0.01    0.16    0.0     \n",
      "Aalto/False_1   48  49  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.14     0.0     0.14    0.0     \n",
      "Aalto/False_1   49  50  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.17     0.01    0.18    0.0     \n",
      "Aalto/False_1   50  51  DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.14     0.0     0.15    0.0     \n",
      "Aalto/False_1   51  52  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.15     0.01    0.16    0.0     \n",
      "Aalto/False_1   52  53  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   53  54  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.01    0.16    0.0     \n",
      "Aalto/False_1   54  55  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   55  56  DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.14     0.0     0.15    0.0     \n",
      "Aalto/False_1   56  57  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.01    0.15    0.0     \n",
      "Aalto/False_1   57  58  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.01    0.15    0.0     \n",
      "Aalto/False_1   58  59  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.16     0.01    0.17    0.0     \n",
      "Aalto/False_1   59  60  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.0     0.16    0.0     \n",
      "Aalto/False_1   60  61  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   61  62  DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.15     0.0     0.15    0.0     \n",
      "Aalto/False_1   62  63  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.16     0.01    0.16    0.0     \n",
      "Aalto/False_1   63  64  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.01    0.15    0.0     \n",
      "Aalto/False_1   64  65  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.14     0.0     0.15    0.0     \n",
      "Aalto/False_1   65  66  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.14     0.0     0.15    0.0     \n",
      "Aalto/False_1   66  67  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.16     0.01    0.17    0.0     \n",
      "Aalto/False_1   67  68  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.17     0.01    0.18    0.0     \n",
      "Aalto/False_1   68  69  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   69  70  DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.15     0.0     0.15    0.0     \n",
      "Aalto/False_1   70  71  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.15     0.0     0.15    0.0     \n",
      "Aalto/False_1   71  72  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.16     0.0     0.17    0.0     \n",
      "Aalto/False_1   72  73  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.16     0.0     0.17    0.0     \n",
      "Aalto/False_1   73  74  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.16     0.01    0.17    0.0     \n",
      "Aalto/False_1   74  75  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.16     0.01    0.17    0.0     \n",
      "Aalto/False_1   75  76  DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.15     0.01    0.16    0.0     \n",
      "Aalto/False_1   76  77  DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.16     0.0     0.17    0.0     \n",
      "Aalto/False_1   77  78  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.0     0.15    0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aalto/False_1   78  79  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.01    0.15    0.0     \n",
      "Aalto/False_1   79  80  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.01    0.15    0.0     \n",
      "Aalto/False_1   80  81  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.0     0.15    0.0     \n",
      "Aalto/False_1   81  82  DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.17     0.0     0.18    0.0     \n",
      "Aalto/False_1   82  83  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.16     0.01    0.17    0.0     \n",
      "Aalto/False_1   83  84  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.0     0.15    0.0     \n",
      "Aalto/False_1   84  85  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.16     0.01    0.17    0.0     \n",
      "Aalto/False_1   85  86  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.16     0.0     0.17    0.0     \n",
      "Aalto/False_1   86  87  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.23     0.0     0.24    0.0     \n",
      "Aalto/False_1   87  88  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.18     0.01    0.18    0.0     \n",
      "Aalto/False_1   88  89  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.15     0.01    0.15    0.0     \n",
      "Aalto/False_1   89  90  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.01    0.16    0.0     \n",
      "Aalto/False_1   90  91  DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.14     0.0     0.15    0.0     \n",
      "Aalto/False_1   91  92  DT      0.93  0.84  0.93  0.84  0.8816   0.91  0.15     0.01    0.16    0.0     \n",
      "Aalto/False_1   92  93  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.16     0.01    0.17    0.0     \n",
      "Aalto/False_1   93  94  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.0     0.15    0.0     \n",
      "Aalto/False_1   94  95  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.25     0.01    0.26    0.0     \n",
      "Aalto/False_1   95  96  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.17     0.0     0.18    0.0     \n",
      "Aalto/False_1   96  97  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.17     0.0     0.17    0.0     \n",
      "Aalto/False_1   97  98  DT      0.93  0.84  0.93  0.84  0.8818   0.91  0.19     0.01    0.19    0.0     \n",
      "Aalto/False_1   98  99  DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.15     0.0     0.15    0.0     \n",
      "Aalto/False_1   99  100 DT      0.93  0.84  0.93  0.84  0.8817   0.91  0.14     0.0     0.15    0.0     \n",
      "                  precision    recall  f1-score      support\n",
      "Aria               1.000000  0.877778  0.934911     90.00000\n",
      "D-LinkCam          0.953825  0.845600  0.896458   1250.00000\n",
      "D-LinkDayCam       0.878049  0.841121  0.859189    214.00000\n",
      "D-LinkDoorSensor   0.785615  0.734637  0.759271    358.00000\n",
      "D-LinkHomeHub      0.882199  0.659334  0.754656   1696.00000\n",
      "D-LinkSensors      0.839151  0.959765  0.895414   5145.00000\n",
      "Edimax             0.928021  0.707843  0.803115    510.00000\n",
      "EdimaxCam          0.938356  0.830303  0.881029    165.00000\n",
      "EdnetCam           0.919355  0.730769  0.814286     78.00000\n",
      "EdnetGateway       0.954545  0.919708  0.936803    137.00000\n",
      "HomeMaticPlug      1.000000  1.000000  1.000000    104.00000\n",
      "Hue-Device         0.991231  0.996563  0.993890   6692.00000\n",
      "IKettle2           0.842105  0.571429  0.680851     28.00000\n",
      "Lightify           0.975515  0.978036  0.976774    774.00000\n",
      "MAXGateway         1.000000  0.931034  0.964286    116.00000\n",
      "SmarterCoffee      0.941176  0.571429  0.711111     28.00000\n",
      "TP-LinkPlugHS      0.934615  0.910112  0.922201    267.00000\n",
      "WeMos              0.963546  0.972623  0.968063   3397.00000\n",
      "Withings           1.000000  1.000000  1.000000    133.00000\n",
      "accuracy           0.928440  0.928440  0.928440      0.92844\n",
      "macro avg          0.933016  0.844110  0.881700  21182.00000\n",
      "weighted avg       0.930492  0.928440  0.926434  21182.00000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "test='Aalto_test_IoTDevID.csv'\n",
    "train='Aalto_BIG_train_IoTDevID.csv'\n",
    "\n",
    "\n",
    "\n",
    "dataset=\"./Aalto combined labels/\"\n",
    "folder(dataset)\n",
    "\n",
    "mixed=False\n",
    "step=1\n",
    "sayac=1\n",
    "output_csv=dataset+str(sayac)+\"_\"+str(step)+\"_\"+str(mixed)+\".csv\"\n",
    "target_names=target_name(test)\n",
    "ML(train,test,output_csv,feature,step,mixed,dataset[2:-1]+\"_\"+str(step))   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
      "Aalto/False_13  0   1   DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.15     0.0     0.15    0.17    \n",
      "Aalto/False_13  1   2   DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.17     0.0     0.17    0.22    \n",
      "Aalto/False_13  2   3   DT      0.97  0.96  0.98  0.96  0.9704   0.96  0.38     0.01    0.39    0.24    \n",
      "Aalto/False_13  3   4   DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.24     0.01    0.25    0.22    \n",
      "Aalto/False_13  4   5   DT      0.97  0.96  0.98  0.96  0.9705   0.96  0.16     0.0     0.17    0.17    \n",
      "Aalto/False_13  5   6   DT      0.97  0.96  0.98  0.96  0.9705   0.96  0.16     0.01    0.17    0.17    \n",
      "Aalto/False_13  6   7   DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.24     0.01    0.25    0.17    \n",
      "Aalto/False_13  7   8   DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.15     0.01    0.15    0.17    \n",
      "Aalto/False_13  8   9   DT      0.97  0.96  0.98  0.96  0.9705   0.96  0.16     0.01    0.17    0.26    \n",
      "Aalto/False_13  9   10  DT      0.97  0.97  0.98  0.97  0.9725   0.96  0.15     0.01    0.16    0.2     \n",
      "Aalto/False_13  10  11  DT      0.97  0.96  0.98  0.96  0.9706   0.96  0.15     0.01    0.16    0.18    \n",
      "Aalto/False_13  11  12  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.19     0.01    0.2     0.19    \n",
      "Aalto/False_13  12  13  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.15     0.01    0.16    0.17    \n",
      "Aalto/False_13  13  14  DT      0.97  0.96  0.98  0.96  0.9712   0.96  0.18     0.01    0.19    0.18    \n",
      "Aalto/False_13  14  15  DT      0.97  0.96  0.98  0.96  0.9705   0.96  0.15     0.01    0.16    0.18    \n",
      "Aalto/False_13  15  16  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.15     0.01    0.16    0.17    \n",
      "Aalto/False_13  16  17  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.16     0.01    0.16    0.15    \n",
      "Aalto/False_13  17  18  DT      0.97  0.96  0.98  0.96  0.9712   0.96  0.15     0.01    0.16    0.16    \n",
      "Aalto/False_13  18  19  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.16     0.01    0.16    0.18    \n",
      "Aalto/False_13  19  20  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.15     0.01    0.16    0.3     \n",
      "Aalto/False_13  20  21  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.19     0.01    0.2     0.3     \n",
      "Aalto/False_13  21  22  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.23     0.01    0.24    0.29    \n",
      "Aalto/False_13  22  23  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.15     0.01    0.16    0.17    \n",
      "Aalto/False_13  23  24  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.22     0.01    0.22    0.24    \n",
      "Aalto/False_13  24  25  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.16     0.01    0.17    0.25    \n",
      "Aalto/False_13  25  26  DT      0.97  0.96  0.98  0.96  0.9712   0.96  0.24     0.0     0.24    0.21    \n",
      "Aalto/False_13  26  27  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.2      0.0     0.21    0.24    \n",
      "Aalto/False_13  27  28  DT      0.97  0.97  0.98  0.97  0.9732   0.96  0.22     0.01    0.22    0.3     \n",
      "Aalto/False_13  28  29  DT      0.97  0.96  0.98  0.96  0.9705   0.96  0.27     0.01    0.27    0.32    \n",
      "Aalto/False_13  29  30  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.28     0.01    0.29    0.38    \n",
      "Aalto/False_13  30  31  DT      0.97  0.96  0.98  0.96  0.9705   0.96  0.2      0.0     0.2     0.16    \n",
      "Aalto/False_13  31  32  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.14     0.01    0.15    0.15    \n",
      "Aalto/False_13  32  33  DT      0.97  0.96  0.98  0.96  0.9706   0.96  0.15     0.01    0.15    0.19    \n",
      "Aalto/False_13  33  34  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.25     0.0     0.25    0.26    \n",
      "Aalto/False_13  34  35  DT      0.97  0.97  0.98  0.97  0.9734   0.96  0.17     0.01    0.18    0.26    \n",
      "Aalto/False_13  35  36  DT      0.97  0.96  0.98  0.96  0.9705   0.96  0.16     0.01    0.17    0.17    \n",
      "Aalto/False_13  36  37  DT      0.97  0.96  0.98  0.96  0.9705   0.96  0.16     0.01    0.16    0.21    \n",
      "Aalto/False_13  37  38  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.15     0.0     0.16    0.2     \n",
      "Aalto/False_13  38  39  DT      0.97  0.97  0.98  0.97  0.9725   0.96  0.15     0.01    0.15    0.15    \n",
      "Aalto/False_13  39  40  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.15     0.01    0.15    0.16    \n",
      "Aalto/False_13  40  41  DT      0.97  0.97  0.98  0.97  0.9732   0.96  0.15     0.0     0.15    0.19    \n",
      "Aalto/False_13  41  42  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.14     0.0     0.15    0.2     \n",
      "Aalto/False_13  42  43  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.14     0.0     0.15    0.17    \n",
      "Aalto/False_13  43  44  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.14     0.01    0.15    0.15    \n",
      "Aalto/False_13  44  45  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.14     0.01    0.15    0.21    \n",
      "Aalto/False_13  45  46  DT      0.97  0.97  0.98  0.97  0.9725   0.96  0.14     0.0     0.15    0.22    \n",
      "Aalto/False_13  46  47  DT      0.97  0.97  0.98  0.97  0.9725   0.96  0.14     0.0     0.15    0.15    \n",
      "Aalto/False_13  47  48  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.14     0.01    0.15    0.15    \n",
      "Aalto/False_13  48  49  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.14     0.0     0.15    0.14    \n",
      "Aalto/False_13  49  50  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.15     0.0     0.15    0.18    \n",
      "Aalto/False_13  50  51  DT      0.97  0.96  0.98  0.96  0.9705   0.96  0.16     0.0     0.16    0.2     \n",
      "Aalto/False_13  51  52  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.15     0.0     0.15    0.19    \n",
      "Aalto/False_13  52  53  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.14     0.0     0.15    0.16    \n",
      "Aalto/False_13  53  54  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.16     0.0     0.16    0.15    \n",
      "Aalto/False_13  54  55  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.16     0.01    0.16    0.21    \n",
      "Aalto/False_13  55  56  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.17     0.01    0.18    0.2     \n",
      "Aalto/False_13  56  57  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.17     0.0     0.17    0.15    \n",
      "Aalto/False_13  57  58  DT      0.97  0.96  0.98  0.96  0.9712   0.96  0.16     0.01    0.16    0.18    \n",
      "Aalto/False_13  58  59  DT      0.97  0.96  0.98  0.96  0.9706   0.96  0.18     0.0     0.18    0.24    \n",
      "Aalto/False_13  59  60  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.15     0.0     0.15    0.21    \n",
      "Aalto/False_13  60  61  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.15     0.01    0.16    0.15    \n",
      "Aalto/False_13  61  62  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.16     0.01    0.16    0.21    \n",
      "Aalto/False_13  62  63  DT      0.97  0.96  0.98  0.96  0.9705   0.96  0.15     0.01    0.16    0.19    \n",
      "Aalto/False_13  63  64  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.14     0.01    0.15    0.15    \n",
      "Aalto/False_13  64  65  DT      0.97  0.96  0.98  0.96  0.9704   0.96  0.15     0.01    0.15    0.17    \n",
      "Aalto/False_13  65  66  DT      0.97  0.97  0.98  0.97  0.9725   0.96  0.15     0.01    0.16    0.17    \n",
      "Aalto/False_13  66  67  DT      0.97  0.97  0.98  0.97  0.9725   0.96  0.15     0.01    0.16    0.16    \n",
      "Aalto/False_13  67  68  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.14     0.01    0.15    0.19    \n",
      "Aalto/False_13  68  69  DT      0.97  0.96  0.98  0.96  0.9705   0.96  0.14     0.01    0.15    0.19    \n",
      "Aalto/False_13  69  70  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.15     0.01    0.15    0.15    \n",
      "Aalto/False_13  70  71  DT      0.97  0.96  0.98  0.96  0.9705   0.96  0.14     0.01    0.15    0.17    \n",
      "Aalto/False_13  71  72  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.15     0.0     0.15    0.17    \n",
      "Aalto/False_13  72  73  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.15     0.01    0.15    0.16    \n",
      "Aalto/False_13  73  74  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.14     0.0     0.15    0.25    \n",
      "Aalto/False_13  74  75  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.15     0.0     0.15    0.2     \n",
      "Aalto/False_13  75  76  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.15     0.0     0.15    0.15    \n",
      "Aalto/False_13  76  77  DT      0.97  0.96  0.98  0.96  0.9706   0.96  0.15     0.0     0.15    0.15    \n",
      "Aalto/False_13  77  78  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.2      0.01    0.21    0.19    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aalto/False_13  78  79  DT      0.97  0.96  0.98  0.96  0.9706   0.96  0.19     0.01    0.2     0.23    \n",
      "Aalto/False_13  79  80  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.14     0.0     0.15    0.21    \n",
      "Aalto/False_13  80  81  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.18     0.01    0.18    0.18    \n",
      "Aalto/False_13  81  82  DT      0.97  0.96  0.98  0.96  0.9712   0.96  0.15     0.0     0.16    0.15    \n",
      "Aalto/False_13  82  83  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.15     0.01    0.16    0.17    \n",
      "Aalto/False_13  83  84  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.15     0.01    0.16    0.18    \n",
      "Aalto/False_13  84  85  DT      0.97  0.96  0.98  0.96  0.9705   0.96  0.18     0.01    0.18    0.15    \n",
      "Aalto/False_13  85  86  DT      0.97  0.96  0.98  0.96  0.9712   0.96  0.14     0.01    0.15    0.18    \n",
      "Aalto/False_13  86  87  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.17     0.01    0.17    0.18    \n",
      "Aalto/False_13  87  88  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.16     0.0     0.17    0.18    \n",
      "Aalto/False_13  88  89  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.17     0.01    0.17    0.19    \n",
      "Aalto/False_13  89  90  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.16     0.0     0.16    0.24    \n",
      "Aalto/False_13  90  91  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.14     0.01    0.15    0.21    \n",
      "Aalto/False_13  91  92  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.15     0.0     0.15    0.15    \n",
      "Aalto/False_13  92  93  DT      0.97  0.97  0.98  0.97  0.9732   0.96  0.14     0.01    0.15    0.15    \n",
      "Aalto/False_13  93  94  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.17     0.01    0.17    0.2     \n",
      "Aalto/False_13  94  95  DT      0.97  0.96  0.98  0.96  0.9705   0.96  0.16     0.0     0.17    0.18    \n",
      "Aalto/False_13  95  96  DT      0.97  0.97  0.98  0.97  0.9725   0.96  0.15     0.0     0.16    0.21    \n",
      "Aalto/False_13  96  97  DT      0.97  0.97  0.98  0.97  0.9726   0.96  0.16     0.0     0.16    0.26    \n",
      "Aalto/False_13  97  98  DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.28     0.01    0.28    0.2     \n",
      "Aalto/False_13  98  99  DT      0.97  0.96  0.98  0.96  0.9713   0.96  0.19     0.01    0.19    0.18    \n",
      "Aalto/False_13  99  100 DT      0.97  0.97  0.98  0.97  0.9733   0.96  0.23     0.0     0.23    0.18    \n",
      "                  precision    recall  f1-score       support\n",
      "Aria               1.000000  1.000000  1.000000     90.000000\n",
      "D-LinkCam          0.976562  1.000000  0.988142   1250.000000\n",
      "D-LinkDayCam       1.000000  1.000000  1.000000    214.000000\n",
      "D-LinkDoorSensor   0.851133  0.734637  0.788606    358.000000\n",
      "D-LinkHomeHub      0.968811  0.659346  0.784668   1696.000000\n",
      "D-LinkSensors      0.906750  1.000000  0.951094   5145.000000\n",
      "Edimax             0.998254  0.962020  0.979758    510.000000\n",
      "EdimaxCam          1.000000  1.000000  1.000000    165.000000\n",
      "EdnetCam           1.000000  1.000000  1.000000     78.000000\n",
      "EdnetGateway       1.000000  1.000000  1.000000    137.000000\n",
      "HomeMaticPlug      1.000000  1.000000  1.000000    104.000000\n",
      "Hue-Device         0.995833  1.000000  0.997912   6692.000000\n",
      "IKettle2           1.000000  1.000000  1.000000     28.000000\n",
      "Lightify           1.000000  1.000000  1.000000    774.000000\n",
      "MAXGateway         1.000000  1.000000  1.000000    116.000000\n",
      "SmarterCoffee      1.000000  0.969286  0.984074     28.000000\n",
      "TP-LinkPlugHS      0.996269  1.000000  0.998131    267.000000\n",
      "WeMos              0.993565  1.000000  0.996772   3397.000000\n",
      "Withings           1.000000  1.000000  1.000000    133.000000\n",
      "accuracy           0.967284  0.967284  0.967284      0.967284\n",
      "macro avg          0.983536  0.964489  0.972061  21182.000000\n",
      "weighted avg       0.968516  0.967284  0.964898  21182.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "test='Aalto_test_IoTDevID.csv'\n",
    "train='Aalto_BIG_train_IoTDevID.csv'\n",
    "\n",
    "\n",
    "dataset=\"./Aalto combined labels/\"\n",
    "folder(dataset)\n",
    "\n",
    "mixed=True\n",
    "step=13\n",
    "sayac=1\n",
    "output_csv=dataset+str(sayac)+\"_\"+str(step)+\"_\"+str(mixed)+\".csv\"\n",
    "target_names=target_name(test)\n",
    "ML(train,test,output_csv,feature,step,mixed,dataset[2:-1]+\"_\"+str(step))   \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}