0003 Algorithm Selection/03. 2 Classification of Individual packets for Aalto Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  importing relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from numpy import array\n",
    "from random import random\n",
    "from sklearn import metrics\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\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",
    "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discovering Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def target_name(name):\n",
    "    df = pd.read_csv(name,usecols=[\"Label\"])\n",
    "    target_names=sorted(list(df[\"Label\"].unique()))\n",
    "    return target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameters of machine learning algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_list={\"NB\":CategoricalNB(alpha=1e-09),\n",
    "\"DT\" :DecisionTreeClassifier(criterion='gini', max_depth=26.0,\n",
    "                       max_features=26,min_samples_split= 6),  \n",
    "\"RF\":RandomForestClassifier(bootstrap= True, criterion= \"gini\", max_depth= 18.0, max_features= 8, min_samples_split= 9, n_estimators= 96),\n",
    "\"KNN\":KNeighborsClassifier(algorithm= 'brute', leaf_size= 41, n_neighbors= 48, weights= 'distance'),\n",
    "\"GB\":GradientBoostingClassifier()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aggregation Algorithm notmal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ML(loop1,loop2,output_csv,cols,step,mixed,dname):\n",
    "\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=100\n",
    "        else:\n",
    "            repetition=100\n",
    "        if ii in [\"MLP\"]: #for slow algorithms.\n",
    "            repetition=10\n",
    "\n",
    "        for i in range(repetition):\n",
    "\n",
    "\n",
    "\n",
    "            #TRAIN\n",
    "            df = pd.read_csv(loop1,usecols=cols)\n",
    "            try:df=df.replace({\"Protocol\": Protocol})\n",
    "            except:pass\n",
    "            m_train=df[\"MAC\"]\n",
    "            del df[\"MAC\"]\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",
    "            try:df=df.replace({\"Protocol\": Protocol})\n",
    "            except:pass\n",
    "            df = shuffle(df)\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",
    "\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 False:\n",
    "            cm=cm//repetition\n",
    "            graph_name=output_csv+ii+\"_confusion matrix.pdf\"   \n",
    "            plt.figure(figsize = (40,28))\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": 13,
   "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": "markdown",
   "metadata": {},
   "source": [
    "# DT & NB & RF &  KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "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_1         0   1   NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.53     0.03    0.56    0.0     \n",
      "Aalto_1         1   2   NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.43     0.03    0.46    0.0     \n",
      "Aalto_1         2   3   NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.45     0.03    0.48    0.0     \n",
      "Aalto_1         3   4   NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.48     0.03    0.51    0.0     \n",
      "Aalto_1         4   5   NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.03    0.44    0.0     \n",
      "Aalto_1         5   6   NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         6   7   NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         7   8   NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.45     0.03    0.48    0.0     \n",
      "Aalto_1         8   9   NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.45    0.0     \n",
      "Aalto_1         9   10  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.45    0.0     \n",
      "Aalto_1         10  11  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.44     0.03    0.47    0.0     \n",
      "Aalto_1         11  12  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.03    0.44    0.0     \n",
      "Aalto_1         12  13  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.43     0.03    0.46    0.0     \n",
      "Aalto_1         13  14  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.44     0.04    0.48    0.0     \n",
      "Aalto_1         14  15  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.45     0.03    0.48    0.0     \n",
      "Aalto_1         15  16  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.43     0.03    0.46    0.0     \n",
      "Aalto_1         16  17  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.45     0.03    0.48    0.0     \n",
      "Aalto_1         17  18  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.43     0.03    0.46    0.0     \n",
      "Aalto_1         18  19  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.43     0.04    0.46    0.0     \n",
      "Aalto_1         19  20  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.44     0.03    0.47    0.0     \n",
      "Aalto_1         20  21  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.43     0.03    0.46    0.0     \n",
      "Aalto_1         21  22  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.7      0.05    0.75    0.0     \n",
      "Aalto_1         22  23  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.44     0.03    0.48    0.0     \n",
      "Aalto_1         23  24  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         24  25  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.44     0.03    0.46    0.0     \n",
      "Aalto_1         25  26  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.03    0.44    0.0     \n",
      "Aalto_1         26  27  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.03    0.44    0.0     \n",
      "Aalto_1         27  28  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         28  29  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.45     0.03    0.48    0.0     \n",
      "Aalto_1         29  30  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.45     0.05    0.49    0.0     \n",
      "Aalto_1         30  31  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.46     0.03    0.49    0.0     \n",
      "Aalto_1         31  32  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.44     0.04    0.48    0.0     \n",
      "Aalto_1         32  33  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.45     0.03    0.48    0.0     \n",
      "Aalto_1         33  34  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.46    0.0     \n",
      "Aalto_1         34  35  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.53     0.06    0.58    0.0     \n",
      "Aalto_1         35  36  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.67     0.03    0.7     0.0     \n",
      "Aalto_1         36  37  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.44     0.03    0.47    0.0     \n",
      "Aalto_1         37  38  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.43     0.03    0.46    0.0     \n",
      "Aalto_1         38  39  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.43     0.03    0.46    0.0     \n",
      "Aalto_1         39  40  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.75     0.06    0.81    0.0     \n",
      "Aalto_1         40  41  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.55     0.03    0.59    0.0     \n",
      "Aalto_1         41  42  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.44     0.06    0.5     0.0     \n",
      "Aalto_1         42  43  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         43  44  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.47     0.03    0.5     0.0     \n",
      "Aalto_1         44  45  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.45    0.0     \n",
      "Aalto_1         45  46  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         46  47  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.45    0.0     \n",
      "Aalto_1         47  48  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.45    0.0     \n",
      "Aalto_1         48  49  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.51     0.03    0.53    0.0     \n",
      "Aalto_1         49  50  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.39     0.03    0.42    0.0     \n",
      "Aalto_1         50  51  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         51  52  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.43     0.03    0.46    0.0     \n",
      "Aalto_1         52  53  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.43     0.03    0.46    0.0     \n",
      "Aalto_1         53  54  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         54  55  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.44     0.03    0.47    0.0     \n",
      "Aalto_1         55  56  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.04    0.46    0.0     \n",
      "Aalto_1         56  57  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.43     0.04    0.48    0.0     \n",
      "Aalto_1         57  58  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.45    0.0     \n",
      "Aalto_1         58  59  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.45    0.0     \n",
      "Aalto_1         59  60  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.45    0.0     \n",
      "Aalto_1         60  61  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.03    0.44    0.0     \n",
      "Aalto_1         61  62  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.44     0.03    0.47    0.0     \n",
      "Aalto_1         62  63  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.04    0.45    0.0     \n",
      "Aalto_1         63  64  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.43     0.03    0.46    0.0     \n",
      "Aalto_1         64  65  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.04    0.43    0.0     \n",
      "Aalto_1         65  66  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         66  67  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.04    0.43    0.0     \n",
      "Aalto_1         67  68  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.03    0.44    0.0     \n",
      "Aalto_1         68  69  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.04    0.43    0.0     \n",
      "Aalto_1         69  70  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.03    0.44    0.0     \n",
      "Aalto_1         70  71  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         71  72  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.44    0.0     \n",
      "Aalto_1         72  73  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.44     0.03    0.47    0.0     \n",
      "Aalto_1         73  74  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.03    0.44    0.0     \n",
      "Aalto_1         74  75  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.45    0.0     \n",
      "Aalto_1         75  76  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.43     0.03    0.47    0.0     \n",
      "Aalto_1         76  77  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         77  78  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.45    0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aalto_1         78  79  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.03    0.44    0.0     \n",
      "Aalto_1         79  80  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.03    0.44    0.0     \n",
      "Aalto_1         80  81  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.46    0.0     \n",
      "Aalto_1         81  82  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.45    0.0     \n",
      "Aalto_1         82  83  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.45    0.0     \n",
      "Aalto_1         83  84  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.45    0.0     \n",
      "Aalto_1         84  85  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.03    0.44    0.0     \n",
      "Aalto_1         85  86  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.03    0.44    0.0     \n",
      "Aalto_1         86  87  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.39     0.03    0.42    0.0     \n",
      "Aalto_1         87  88  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.42     0.03    0.45    0.0     \n",
      "Aalto_1         88  89  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         89  90  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         90  91  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.03    0.44    0.0     \n",
      "Aalto_1         91  92  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         92  93  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         93  94  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         94  95  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         95  96  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.41     0.03    0.43    0.0     \n",
      "Aalto_1         96  97  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         97  98  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         98  99  NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.4      0.03    0.43    0.0     \n",
      "Aalto_1         99  100 NB      0.62  0.63  0.58  0.63  0.5587   0.58  0.39     0.03    0.43    0.0     \n",
      "                   precision    recall  f1-score       support\n",
      "Aria                1.000000  0.666667  0.800000     90.000000\n",
      "D-LinkCam           0.810762  0.723200  0.764482   1250.000000\n",
      "D-LinkDayCam        0.342246  0.897196  0.495484    214.000000\n",
      "D-LinkDoorSensor    0.343949  0.905028  0.498462    358.000000\n",
      "D-LinkHomeHub       0.800755  0.500000  0.615608   1696.000000\n",
      "D-LinkSensor        0.394521  0.321190  0.354098   1345.000000\n",
      "D-LinkSiren         0.271930  0.025061  0.045892   1237.000000\n",
      "D-LinkSwitch        0.393838  0.716667  0.508329   1320.000000\n",
      "D-LinkWaterSensor   0.293367  0.185036  0.226936   1243.000000\n",
      "EdimaxCam           0.721212  0.721212  0.721212    165.000000\n",
      "EdimaxPlug1101W     0.373563  0.492424  0.424837    264.000000\n",
      "EdimaxPlug2101W     0.268817  0.304878  0.285714    246.000000\n",
      "EdnetCam            0.165394  0.833333  0.276008     78.000000\n",
      "EdnetGateway        0.966942  0.854015  0.906977    137.000000\n",
      "HomeMaticPlug       1.000000  1.000000  1.000000    104.000000\n",
      "HueBridge           0.923843  0.641549  0.757242   2893.000000\n",
      "HueSwitch           0.799697  0.973151  0.877939   3799.000000\n",
      "IKettle2            0.085561  0.571429  0.148837     28.000000\n",
      "Lightify            0.997245  0.935401  0.965333    774.000000\n",
      "MAXGateway          1.000000  0.931034  0.964286    116.000000\n",
      "SmarterCoffee       0.117647  0.285714  0.166667     28.000000\n",
      "TP-LinkPlugHS100    0.300275  0.762238  0.430830    143.000000\n",
      "TP-LinkPlugHS110    0.714286  0.161290  0.263158    124.000000\n",
      "WeMoInsightSwitch   0.747782  0.480848  0.585317   1227.000000\n",
      "WeMoLink            0.492153  0.576471  0.530985   1360.000000\n",
      "WeMoSwitch          0.454012  0.572840  0.506550    810.000000\n",
      "Withings            1.000000  0.932331  0.964981    133.000000\n",
      "accuracy            0.617364  0.617364  0.617364      0.617364\n",
      "macro avg           0.584437  0.628526  0.558747  21182.000000\n",
      "weighted avg        0.649890  0.617364  0.607145  21182.000000\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 2880x2016 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "Aalto_1         0   1   DT      0.7   0.71  0.78  0.71  0.7272   0.68  0.16     0.0     0.16    0.0     \n",
      "Aalto_1         1   2   DT      0.71  0.71  0.78  0.71  0.7278   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         2   3   DT      0.71  0.71  0.78  0.71  0.7279   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         3   4   DT      0.7   0.71  0.78  0.71  0.7275   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         4   5   DT      0.71  0.71  0.78  0.71  0.7276   0.68  0.13     0.0     0.14    0.0     \n",
      "Aalto_1         5   6   DT      0.71  0.71  0.78  0.71  0.7283   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         6   7   DT      0.71  0.71  0.77  0.71  0.7265   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         7   8   DT      0.71  0.71  0.78  0.71  0.7277   0.68  0.13     0.0     0.14    0.0     \n",
      "Aalto_1         8   9   DT      0.71  0.71  0.78  0.71  0.7283   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         9   10  DT      0.71  0.71  0.77  0.71  0.7265   0.68  0.12     0.0     0.12    0.0     \n",
      "Aalto_1         10  11  DT      0.71  0.71  0.77  0.71  0.7265   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         11  12  DT      0.71  0.71  0.78  0.71  0.7279   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         12  13  DT      0.71  0.71  0.78  0.71  0.7273   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         13  14  DT      0.71  0.71  0.77  0.71  0.7259   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         14  15  DT      0.7   0.71  0.77  0.71  0.7265   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         15  16  DT      0.71  0.71  0.78  0.71  0.7279   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         16  17  DT      0.71  0.7   0.77  0.7   0.7267   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         17  18  DT      0.71  0.7   0.77  0.7   0.7252   0.68  0.13     0.0     0.14    0.0     \n",
      "Aalto_1         18  19  DT      0.71  0.7   0.76  0.7   0.7238   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         19  20  DT      0.71  0.71  0.78  0.71  0.7295   0.68  0.13     0.01    0.13    0.0     \n",
      "Aalto_1         20  21  DT      0.71  0.71  0.78  0.71  0.7286   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         21  22  DT      0.71  0.71  0.78  0.71  0.7285   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         22  23  DT      0.7   0.71  0.78  0.71  0.7277   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         23  24  DT      0.71  0.7   0.77  0.7   0.7266   0.68  0.14     0.0     0.14    0.0     \n",
      "Aalto_1         24  25  DT      0.71  0.71  0.78  0.71  0.7285   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         25  26  DT      0.7   0.71  0.77  0.71  0.7271   0.68  0.13     0.0     0.14    0.0     \n",
      "Aalto_1         26  27  DT      0.71  0.71  0.77  0.71  0.7262   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         27  28  DT      0.71  0.71  0.78  0.71  0.7287   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         28  29  DT      0.71  0.7   0.78  0.7   0.7283   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         29  30  DT      0.7   0.71  0.78  0.71  0.7274   0.68  0.14     0.0     0.14    0.0     \n",
      "Aalto_1         30  31  DT      0.71  0.71  0.78  0.71  0.7286   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         31  32  DT      0.7   0.71  0.77  0.71  0.7259   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         32  33  DT      0.7   0.71  0.77  0.71  0.7269   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         33  34  DT      0.7   0.71  0.78  0.71  0.7275   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         34  35  DT      0.71  0.71  0.78  0.71  0.7289   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         35  36  DT      0.71  0.71  0.78  0.71  0.7276   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         36  37  DT      0.71  0.71  0.78  0.71  0.7296   0.68  0.12     0.0     0.12    0.0     \n",
      "Aalto_1         37  38  DT      0.7   0.71  0.78  0.71  0.7279   0.68  0.12     0.0     0.12    0.0     \n",
      "Aalto_1         38  39  DT      0.71  0.71  0.77  0.71  0.7272   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         39  40  DT      0.7   0.7   0.77  0.7   0.7269   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         40  41  DT      0.7   0.71  0.78  0.71  0.7279   0.68  0.13     0.0     0.14    0.0     \n",
      "Aalto_1         41  42  DT      0.71  0.71  0.77  0.71  0.7268   0.68  0.13     0.0     0.14    0.0     \n",
      "Aalto_1         42  43  DT      0.71  0.71  0.77  0.71  0.7268   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         43  44  DT      0.71  0.7   0.77  0.7   0.7234   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         44  45  DT      0.71  0.71  0.78  0.71  0.7294   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         45  46  DT      0.71  0.7   0.77  0.7   0.7255   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         46  47  DT      0.7   0.71  0.77  0.71  0.7273   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         47  48  DT      0.71  0.71  0.78  0.71  0.7281   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         48  49  DT      0.7   0.71  0.77  0.71  0.7271   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         49  50  DT      0.7   0.7   0.77  0.7   0.7256   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         50  51  DT      0.71  0.71  0.77  0.71  0.7265   0.68  0.13     0.0     0.14    0.0     \n",
      "Aalto_1         51  52  DT      0.71  0.71  0.77  0.71  0.7261   0.68  0.13     0.0     0.14    0.0     \n",
      "Aalto_1         52  53  DT      0.71  0.71  0.78  0.71  0.7279   0.68  0.14     0.01    0.14    0.0     \n",
      "Aalto_1         53  54  DT      0.7   0.7   0.77  0.7   0.7262   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         54  55  DT      0.71  0.71  0.78  0.71  0.7282   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         55  56  DT      0.7   0.7   0.78  0.7   0.7272   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         56  57  DT      0.71  0.71  0.78  0.71  0.7285   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         57  58  DT      0.71  0.71  0.78  0.71  0.7283   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         58  59  DT      0.71  0.71  0.77  0.71  0.725    0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         59  60  DT      0.71  0.71  0.78  0.71  0.7284   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         60  61  DT      0.71  0.71  0.78  0.71  0.7283   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         61  62  DT      0.71  0.71  0.77  0.71  0.7268   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         62  63  DT      0.71  0.71  0.77  0.71  0.7276   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         63  64  DT      0.71  0.71  0.78  0.71  0.728    0.68  0.13     0.0     0.14    0.0     \n",
      "Aalto_1         64  65  DT      0.71  0.71  0.77  0.71  0.727    0.68  0.14     0.0     0.14    0.0     \n",
      "Aalto_1         65  66  DT      0.71  0.71  0.78  0.71  0.7277   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         66  67  DT      0.71  0.71  0.77  0.71  0.7261   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         67  68  DT      0.71  0.7   0.78  0.7   0.7275   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         68  69  DT      0.71  0.71  0.77  0.71  0.7274   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         69  70  DT      0.7   0.7   0.77  0.7   0.7269   0.68  0.13     0.0     0.14    0.0     \n",
      "Aalto_1         70  71  DT      0.71  0.71  0.77  0.71  0.7261   0.68  0.12     0.0     0.12    0.0     \n",
      "Aalto_1         71  72  DT      0.7   0.71  0.78  0.71  0.7276   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         72  73  DT      0.71  0.7   0.78  0.7   0.7286   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         73  74  DT      0.71  0.71  0.78  0.71  0.7281   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         74  75  DT      0.7   0.71  0.77  0.71  0.7274   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         75  76  DT      0.71  0.7   0.77  0.7   0.7264   0.68  0.14     0.0     0.14    0.0     \n",
      "Aalto_1         76  77  DT      0.71  0.71  0.78  0.71  0.7285   0.68  0.13     0.0     0.13    0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aalto_1         77  78  DT      0.71  0.71  0.78  0.71  0.7282   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         78  79  DT      0.71  0.71  0.77  0.71  0.7277   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         79  80  DT      0.7   0.71  0.78  0.71  0.7269   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         80  81  DT      0.71  0.71  0.77  0.71  0.7271   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         81  82  DT      0.7   0.7   0.77  0.7   0.7261   0.67  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         82  83  DT      0.71  0.71  0.78  0.71  0.7282   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         83  84  DT      0.71  0.71  0.77  0.71  0.7254   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         84  85  DT      0.71  0.7   0.77  0.7   0.7266   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         85  86  DT      0.71  0.71  0.77  0.71  0.7258   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         86  87  DT      0.7   0.71  0.77  0.71  0.7252   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         87  88  DT      0.71  0.71  0.77  0.71  0.727    0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         88  89  DT      0.71  0.71  0.78  0.71  0.7276   0.68  0.13     0.0     0.14    0.0     \n",
      "Aalto_1         89  90  DT      0.71  0.7   0.78  0.7   0.7275   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         90  91  DT      0.71  0.71  0.77  0.71  0.7266   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         91  92  DT      0.71  0.71  0.78  0.71  0.7281   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         92  93  DT      0.71  0.71  0.78  0.71  0.7281   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         93  94  DT      0.7   0.71  0.78  0.71  0.7267   0.68  0.12     0.0     0.12    0.0     \n",
      "Aalto_1         94  95  DT      0.7   0.71  0.78  0.71  0.7276   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         95  96  DT      0.7   0.7   0.77  0.7   0.7261   0.68  0.12     0.0     0.13    0.0     \n",
      "Aalto_1         96  97  DT      0.71  0.7   0.77  0.7   0.7256   0.68  0.13     0.0     0.14    0.0     \n",
      "Aalto_1         97  98  DT      0.7   0.7   0.77  0.7   0.7257   0.68  0.13     0.0     0.14    0.0     \n",
      "Aalto_1         98  99  DT      0.71  0.71  0.78  0.71  0.7287   0.68  0.13     0.0     0.13    0.0     \n",
      "Aalto_1         99  100 DT      0.7   0.7   0.77  0.7   0.7248   0.67  0.12     0.01    0.13    0.0     \n",
      "                   precision    recall  f1-score       support\n",
      "Aria                0.993821  0.877778  0.932120     90.000000\n",
      "D-LinkCam           0.936389  0.849400  0.890775   1250.000000\n",
      "D-LinkDayCam        0.892658  0.836449  0.863623    214.000000\n",
      "D-LinkDoorSensor    0.783940  0.741480  0.761772    358.000000\n",
      "D-LinkHomeHub       0.569180  0.847476  0.680948   1696.000000\n",
      "D-LinkSensor        0.403843  0.362290  0.381913   1345.000000\n",
      "D-LinkSiren         0.443090  0.313185  0.366916   1237.000000\n",
      "D-LinkSwitch        0.763544  0.588386  0.664557   1320.000000\n",
      "D-LinkWaterSensor   0.375993  0.408689  0.391645   1243.000000\n",
      "EdimaxCam           0.910084  0.838000  0.872473    165.000000\n",
      "EdimaxPlug1101W     0.534084  0.686364  0.600622    264.000000\n",
      "EdimaxPlug2101W     0.582818  0.372358  0.453066    246.000000\n",
      "EdnetCam            0.970115  0.730769  0.833245     78.000000\n",
      "EdnetGateway        0.902933  0.912409  0.907626    137.000000\n",
      "HomeMaticPlug       1.000000  1.000000  1.000000    104.000000\n",
      "HueBridge           0.923747  0.721925  0.810460   2893.000000\n",
      "HueSwitch           0.824255  0.968692  0.890655   3799.000000\n",
      "IKettle2            0.997778  0.571429  0.726640     28.000000\n",
      "Lightify            0.977921  0.976770  0.977338    774.000000\n",
      "MAXGateway          1.000000  0.931034  0.964286    116.000000\n",
      "SmarterCoffee       1.000000  0.571429  0.727273     28.000000\n",
      "TP-LinkPlugHS100    0.618561  0.788182  0.693116    143.000000\n",
      "TP-LinkPlugHS110    0.689383  0.311210  0.428628    124.000000\n",
      "WeMoInsightSwitch   0.738763  0.607547  0.666702   1227.000000\n",
      "WeMoLink            0.610427  0.667669  0.637761   1360.000000\n",
      "WeMoSwitch          0.463184  0.568728  0.510550    810.000000\n",
      "Withings            1.000000  1.000000  1.000000    133.000000\n",
      "accuracy            0.705411  0.705411  0.705411      0.705411\n",
      "macro avg           0.774315  0.705542  0.727212  21182.000000\n",
      "weighted avg        0.715358  0.705411  0.702019  21182.000000\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 2880x2016 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "Aalto_1         0   1   RF      0.71  0.71  0.77  0.71  0.7279   0.68  3.74     0.34    4.07    0.0     \n",
      "Aalto_1         1   2   RF      0.71  0.71  0.77  0.71  0.7302   0.68  3.73     0.34    4.07    0.0     \n",
      "Aalto_1         2   3   RF      0.71  0.71  0.77  0.71  0.728    0.68  3.72     0.33    4.05    0.0     \n",
      "Aalto_1         3   4   RF      0.71  0.71  0.77  0.71  0.7275   0.68  3.79     0.34    4.13    0.0     \n",
      "Aalto_1         4   5   RF      0.71  0.71  0.77  0.71  0.728    0.68  3.73     0.33    4.05    0.0     \n",
      "Aalto_1         5   6   RF      0.71  0.71  0.77  0.71  0.7295   0.68  3.72     0.33    4.05    0.0     \n",
      "Aalto_1         6   7   RF      0.71  0.71  0.77  0.71  0.7277   0.68  3.74     0.33    4.07    0.0     \n",
      "Aalto_1         7   8   RF      0.71  0.71  0.77  0.71  0.7257   0.68  3.79     0.33    4.12    0.0     \n",
      "Aalto_1         8   9   RF      0.71  0.71  0.77  0.71  0.7276   0.68  3.72     0.33    4.06    0.0     \n",
      "Aalto_1         9   10  RF      0.71  0.71  0.76  0.71  0.7235   0.68  3.72     0.34    4.06    0.0     \n",
      "Aalto_1         10  11  RF      0.71  0.71  0.76  0.71  0.7259   0.68  3.73     0.33    4.06    0.0     \n",
      "Aalto_1         11  12  RF      0.71  0.71  0.77  0.71  0.7274   0.68  3.74     0.33    4.07    0.0     \n",
      "Aalto_1         12  13  RF      0.71  0.71  0.77  0.71  0.7298   0.68  3.77     0.34    4.11    0.0     \n",
      "Aalto_1         13  14  RF      0.71  0.71  0.77  0.71  0.7272   0.68  3.77     0.33    4.1     0.0     \n",
      "Aalto_1         14  15  RF      0.71  0.71  0.77  0.71  0.726    0.68  3.71     0.34    4.05    0.0     \n",
      "Aalto_1         15  16  RF      0.71  0.71  0.77  0.71  0.7273   0.68  3.74     0.34    4.08    0.0     \n",
      "Aalto_1         16  17  RF      0.71  0.71  0.77  0.71  0.7265   0.68  3.75     0.33    4.08    0.0     \n",
      "Aalto_1         17  18  RF      0.71  0.71  0.76  0.71  0.7234   0.68  3.71     0.34    4.05    0.0     \n",
      "Aalto_1         18  19  RF      0.71  0.71  0.77  0.71  0.7279   0.68  3.73     0.33    4.06    0.0     \n",
      "Aalto_1         19  20  RF      0.71  0.71  0.78  0.71  0.7302   0.68  3.77     0.34    4.1     0.0     \n",
      "Aalto_1         20  21  RF      0.71  0.71  0.77  0.71  0.7246   0.68  3.74     0.34    4.07    0.0     \n",
      "Aalto_1         21  22  RF      0.71  0.71  0.77  0.71  0.7274   0.68  3.78     0.33    4.11    0.0     \n",
      "Aalto_1         22  23  RF      0.71  0.71  0.76  0.71  0.7257   0.68  3.77     0.35    4.12    0.0     \n",
      "Aalto_1         23  24  RF      0.71  0.71  0.77  0.71  0.7273   0.68  3.7      0.33    4.04    0.0     \n",
      "Aalto_1         24  25  RF      0.71  0.71  0.77  0.71  0.7268   0.68  3.84     0.33    4.18    0.0     \n",
      "Aalto_1         25  26  RF      0.71  0.71  0.77  0.71  0.7278   0.68  3.78     0.34    4.12    0.0     \n",
      "Aalto_1         26  27  RF      0.71  0.71  0.77  0.71  0.7264   0.68  3.79     0.34    4.12    0.0     \n",
      "Aalto_1         27  28  RF      0.71  0.71  0.77  0.71  0.7256   0.68  3.73     0.34    4.07    0.0     \n",
      "Aalto_1         28  29  RF      0.71  0.71  0.76  0.71  0.7263   0.68  3.74     0.33    4.07    0.0     \n",
      "Aalto_1         29  30  RF      0.71  0.71  0.77  0.71  0.7293   0.68  3.8      0.33    4.14    0.0     \n",
      "Aalto_1         30  31  RF      0.71  0.71  0.77  0.71  0.7281   0.68  3.78     0.35    4.13    0.0     \n",
      "Aalto_1         31  32  RF      0.71  0.71  0.76  0.71  0.7256   0.68  3.76     0.34    4.1     0.0     \n",
      "Aalto_1         32  33  RF      0.71  0.71  0.77  0.71  0.7267   0.68  3.78     0.33    4.11    0.0     \n",
      "Aalto_1         33  34  RF      0.71  0.71  0.76  0.71  0.7241   0.68  3.79     0.33    4.12    0.0     \n",
      "Aalto_1         34  35  RF      0.71  0.71  0.77  0.71  0.7286   0.68  3.75     0.33    4.09    0.0     \n",
      "Aalto_1         35  36  RF      0.71  0.71  0.77  0.71  0.7282   0.68  3.76     0.34    4.09    0.0     \n",
      "Aalto_1         36  37  RF      0.71  0.71  0.77  0.71  0.7271   0.68  3.75     0.34    4.1     0.0     \n",
      "Aalto_1         37  38  RF      0.71  0.71  0.77  0.71  0.7283   0.68  3.77     0.33    4.1     0.0     \n",
      "Aalto_1         38  39  RF      0.71  0.71  0.77  0.71  0.7255   0.68  3.77     0.33    4.09    0.0     \n",
      "Aalto_1         39  40  RF      0.71  0.71  0.77  0.71  0.7284   0.68  3.76     0.33    4.1     0.0     \n",
      "Aalto_1         40  41  RF      0.71  0.71  0.77  0.71  0.7288   0.68  3.75     0.33    4.09    0.0     \n",
      "Aalto_1         41  42  RF      0.71  0.71  0.77  0.71  0.7275   0.68  3.75     0.34    4.09    0.0     \n",
      "Aalto_1         42  43  RF      0.71  0.71  0.77  0.71  0.7278   0.68  3.73     0.33    4.06    0.0     \n",
      "Aalto_1         43  44  RF      0.71  0.71  0.77  0.71  0.7283   0.68  3.75     0.33    4.07    0.0     \n",
      "Aalto_1         44  45  RF      0.71  0.71  0.77  0.71  0.727    0.68  3.77     0.33    4.1     0.0     \n",
      "Aalto_1         45  46  RF      0.71  0.71  0.77  0.71  0.7267   0.68  3.75     0.34    4.09    0.0     \n",
      "Aalto_1         46  47  RF      0.71  0.71  0.77  0.71  0.7286   0.68  3.73     0.34    4.07    0.0     \n",
      "Aalto_1         47  48  RF      0.71  0.71  0.77  0.71  0.7269   0.68  3.76     0.34    4.1     0.0     \n",
      "Aalto_1         48  49  RF      0.71  0.71  0.77  0.71  0.7276   0.68  3.82     0.34    4.16    0.0     \n",
      "Aalto_1         49  50  RF      0.71  0.71  0.77  0.71  0.7299   0.68  3.82     0.37    4.19    0.0     \n",
      "Aalto_1         50  51  RF      0.71  0.71  0.77  0.71  0.728    0.68  4.04     0.35    4.39    0.0     \n",
      "Aalto_1         51  52  RF      0.71  0.71  0.77  0.71  0.7254   0.68  3.9      0.33    4.22    0.0     \n",
      "Aalto_1         52  53  RF      0.71  0.71  0.76  0.71  0.7265   0.68  3.71     0.33    4.04    0.0     \n",
      "Aalto_1         53  54  RF      0.71  0.71  0.77  0.71  0.7287   0.68  3.74     0.32    4.06    0.0     \n",
      "Aalto_1         54  55  RF      0.71  0.71  0.76  0.71  0.7247   0.68  3.69     0.33    4.02    0.0     \n",
      "Aalto_1         55  56  RF      0.71  0.71  0.77  0.71  0.7253   0.68  3.71     0.33    4.04    0.0     \n",
      "Aalto_1         56  57  RF      0.71  0.71  0.76  0.71  0.7257   0.68  3.73     0.33    4.06    0.0     \n",
      "Aalto_1         57  58  RF      0.71  0.71  0.77  0.71  0.7276   0.68  3.7      0.33    4.02    0.0     \n",
      "Aalto_1         58  59  RF      0.71  0.71  0.77  0.71  0.7261   0.68  3.67     0.33    4.0     0.0     \n",
      "Aalto_1         59  60  RF      0.71  0.71  0.77  0.71  0.7297   0.68  3.75     0.33    4.08    0.0     \n",
      "Aalto_1         60  61  RF      0.71  0.71  0.77  0.71  0.7247   0.68  3.69     0.34    4.03    0.0     \n",
      "Aalto_1         61  62  RF      0.71  0.71  0.76  0.71  0.7251   0.68  3.72     0.32    4.04    0.0     \n",
      "Aalto_1         62  63  RF      0.71  0.71  0.77  0.71  0.7291   0.68  3.67     0.32    3.99    0.0     \n",
      "Aalto_1         63  64  RF      0.71  0.71  0.77  0.71  0.7277   0.68  3.7      0.33    4.03    0.0     \n",
      "Aalto_1         64  65  RF      0.71  0.71  0.77  0.71  0.7259   0.68  3.73     0.33    4.06    0.0     \n",
      "Aalto_1         65  66  RF      0.71  0.71  0.76  0.71  0.7262   0.68  3.69     0.32    4.02    0.0     \n",
      "Aalto_1         66  67  RF      0.71  0.71  0.77  0.71  0.7281   0.68  3.72     0.33    4.04    0.0     \n",
      "Aalto_1         67  68  RF      0.71  0.71  0.77  0.71  0.7292   0.68  3.71     0.32    4.03    0.0     \n",
      "Aalto_1         68  69  RF      0.71  0.71  0.77  0.71  0.7279   0.68  3.75     0.33    4.07    0.0     \n",
      "Aalto_1         69  70  RF      0.71  0.71  0.77  0.71  0.7257   0.68  3.7      0.33    4.02    0.0     \n",
      "Aalto_1         70  71  RF      0.71  0.71  0.77  0.71  0.726    0.68  3.69     0.33    4.02    0.0     \n",
      "Aalto_1         71  72  RF      0.71  0.71  0.77  0.71  0.7254   0.68  3.68     0.33    4.01    0.0     \n",
      "Aalto_1         72  73  RF      0.71  0.71  0.77  0.71  0.7291   0.68  3.73     0.33    4.06    0.0     \n",
      "Aalto_1         73  74  RF      0.71  0.71  0.76  0.71  0.7252   0.68  3.71     0.33    4.03    0.0     \n",
      "Aalto_1         74  75  RF      0.71  0.71  0.77  0.71  0.7269   0.68  3.68     0.32    4.0     0.0     \n",
      "Aalto_1         75  76  RF      0.71  0.71  0.77  0.71  0.7297   0.68  3.8      0.33    4.12    0.0     \n",
      "Aalto_1         76  77  RF      0.71  0.71  0.77  0.71  0.7267   0.68  3.72     0.33    4.05    0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aalto_1         77  78  RF      0.71  0.71  0.78  0.71  0.7302   0.68  3.67     0.34    4.0     0.0     \n",
      "Aalto_1         78  79  RF      0.71  0.71  0.77  0.71  0.7259   0.68  3.7      0.33    4.02    0.0     \n",
      "Aalto_1         79  80  RF      0.71  0.71  0.76  0.71  0.725    0.68  3.75     0.34    4.09    0.0     \n",
      "Aalto_1         80  81  RF      0.71  0.71  0.77  0.71  0.7286   0.68  3.91     0.33    4.24    0.0     \n",
      "Aalto_1         81  82  RF      0.71  0.71  0.77  0.71  0.7261   0.68  3.73     0.33    4.06    0.0     \n",
      "Aalto_1         82  83  RF      0.71  0.71  0.77  0.71  0.7288   0.68  3.7      0.33    4.03    0.0     \n",
      "Aalto_1         83  84  RF      0.71  0.71  0.76  0.71  0.7248   0.68  3.72     0.33    4.04    0.0     \n",
      "Aalto_1         84  85  RF      0.71  0.71  0.77  0.71  0.7285   0.68  3.72     0.32    4.05    0.0     \n",
      "Aalto_1         85  86  RF      0.71  0.71  0.77  0.71  0.7268   0.68  3.7      0.33    4.03    0.0     \n",
      "Aalto_1         86  87  RF      0.71  0.71  0.77  0.71  0.7279   0.68  3.72     0.33    4.04    0.0     \n",
      "Aalto_1         87  88  RF      0.71  0.71  0.77  0.71  0.7287   0.68  3.69     0.33    4.01    0.0     \n",
      "Aalto_1         88  89  RF      0.71  0.71  0.77  0.71  0.7285   0.68  3.69     0.34    4.03    0.0     \n",
      "Aalto_1         89  90  RF      0.71  0.71  0.77  0.71  0.7271   0.68  3.71     0.33    4.04    0.0     \n",
      "Aalto_1         90  91  RF      0.71  0.71  0.77  0.71  0.7271   0.68  3.72     0.33    4.05    0.0     \n",
      "Aalto_1         91  92  RF      0.71  0.71  0.77  0.71  0.727    0.68  3.73     0.33    4.05    0.0     \n",
      "Aalto_1         92  93  RF      0.71  0.71  0.77  0.71  0.7271   0.68  3.69     0.33    4.01    0.0     \n",
      "Aalto_1         93  94  RF      0.71  0.71  0.77  0.71  0.7301   0.68  3.72     0.34    4.05    0.0     \n",
      "Aalto_1         94  95  RF      0.71  0.71  0.77  0.71  0.7274   0.68  3.66     0.34    3.99    0.0     \n",
      "Aalto_1         95  96  RF      0.71  0.71  0.77  0.71  0.7261   0.68  3.73     0.33    4.06    0.0     \n",
      "Aalto_1         96  97  RF      0.71  0.71  0.76  0.71  0.7251   0.68  3.69     0.33    4.02    0.0     \n",
      "Aalto_1         97  98  RF      0.71  0.71  0.77  0.71  0.7265   0.68  3.74     0.33    4.08    0.0     \n",
      "Aalto_1         98  99  RF      0.71  0.71  0.77  0.71  0.7278   0.68  3.82     0.36    4.18    0.0     \n",
      "Aalto_1         99  100 RF      0.71  0.71  0.77  0.71  0.7258   0.68  3.75     0.33    4.09    0.0     \n",
      "                   precision    recall  f1-score       support\n",
      "Aria                0.970339  0.877778  0.921670     90.000000\n",
      "D-LinkCam           0.933891  0.849176  0.889521   1250.000000\n",
      "D-LinkDayCam        0.881573  0.831308  0.855700    214.000000\n",
      "D-LinkDoorSensor    0.802839  0.748408  0.774657    358.000000\n",
      "D-LinkHomeHub       0.577462  0.844782  0.685989   1696.000000\n",
      "D-LinkSensor        0.439630  0.336401  0.377944   1345.000000\n",
      "D-LinkSiren         0.456341  0.301997  0.363222   1237.000000\n",
      "D-LinkSwitch        0.695827  0.613742  0.648825   1320.000000\n",
      "D-LinkWaterSensor   0.368071  0.424916  0.394412   1243.000000\n",
      "EdimaxCam           0.936639  0.830303  0.880268    165.000000\n",
      "EdimaxPlug1101W     0.569384  0.628523  0.596549    264.000000\n",
      "EdimaxPlug2101W     0.553237  0.422805  0.477620    246.000000\n",
      "EdnetCam            0.867971  0.731282  0.793233     78.000000\n",
      "EdnetGateway        0.886520  0.918613  0.902279    137.000000\n",
      "HomeMaticPlug       1.000000  1.000000  1.000000    104.000000\n",
      "HueBridge           0.929987  0.722050  0.812932   2893.000000\n",
      "HueSwitch           0.825024  0.973348  0.893070   3799.000000\n",
      "IKettle2            0.928307  0.571429  0.706086     28.000000\n",
      "Lightify            0.959651  0.976873  0.968176    774.000000\n",
      "MAXGateway          1.000000  0.931034  0.964286    116.000000\n",
      "SmarterCoffee       0.990131  0.571429  0.724539     28.000000\n",
      "TP-LinkPlugHS100    0.633091  0.793846  0.703009    143.000000\n",
      "TP-LinkPlugHS110    0.669985  0.346371  0.453949    124.000000\n",
      "WeMoInsightSwitch   0.799076  0.609527  0.691363   1227.000000\n",
      "WeMoLink            0.598229  0.718360  0.652762   1360.000000\n",
      "WeMoSwitch          0.473183  0.534333  0.501665    810.000000\n",
      "Withings            1.000000  1.000000  1.000000    133.000000\n",
      "accuracy            0.708484  0.708484  0.708484      0.708484\n",
      "macro avg           0.768385  0.707727  0.727175  21182.000000\n",
      "weighted avg        0.717459  0.708484  0.703941  21182.000000\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 2880x2016 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "Aalto_1         0   1   KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.27   20.27   0.0     \n",
      "Aalto_1         1   2   KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.16   20.17   0.0     \n",
      "Aalto_1         2   3   KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.01     20.19   20.2    0.0     \n",
      "Aalto_1         3   4   KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.18   20.18   0.0     \n",
      "Aalto_1         4   5   KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.01     20.17   20.17   0.0     \n",
      "Aalto_1         5   6   KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.17   20.18   0.0     \n",
      "Aalto_1         6   7   KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.26   20.26   0.0     \n",
      "Aalto_1         7   8   KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.18   20.18   0.0     \n",
      "Aalto_1         8   9   KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.01     20.14   20.14   0.0     \n",
      "Aalto_1         9   10  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.15   20.15   0.0     \n",
      "Aalto_1         10  11  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.18   20.18   0.0     \n",
      "Aalto_1         11  12  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.16   20.16   0.0     \n",
      "Aalto_1         12  13  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.23   20.24   0.0     \n",
      "Aalto_1         13  14  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.19   20.19   0.0     \n",
      "Aalto_1         14  15  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.24   20.24   0.0     \n",
      "Aalto_1         15  16  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.19   20.2    0.0     \n",
      "Aalto_1         16  17  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.18   20.18   0.0     \n",
      "Aalto_1         17  18  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.28   20.29   0.0     \n",
      "Aalto_1         18  19  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.21   20.21   0.0     \n",
      "Aalto_1         19  20  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.16   20.17   0.0     \n",
      "Aalto_1         20  21  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.01     20.19   20.19   0.0     \n",
      "Aalto_1         21  22  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.14   20.15   0.0     \n",
      "Aalto_1         22  23  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.19   20.2    0.0     \n",
      "Aalto_1         23  24  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.27   20.28   0.0     \n",
      "Aalto_1         24  25  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.18   20.19   0.0     \n",
      "Aalto_1         25  26  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.18   20.18   0.0     \n",
      "Aalto_1         26  27  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.18   20.18   0.0     \n",
      "Aalto_1         27  28  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.18   20.18   0.0     \n",
      "Aalto_1         28  29  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.24   20.24   0.0     \n",
      "Aalto_1         29  30  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.22   20.22   0.0     \n",
      "Aalto_1         30  31  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.2    20.2    0.0     \n",
      "Aalto_1         31  32  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.17   20.18   0.0     \n",
      "Aalto_1         32  33  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.01     20.19   20.2    0.0     \n",
      "Aalto_1         33  34  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.19   20.2    0.0     \n",
      "Aalto_1         34  35  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.01     20.19   20.19   0.0     \n",
      "Aalto_1         35  36  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.18   20.19   0.0     \n",
      "Aalto_1         36  37  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.17   20.18   0.0     \n",
      "Aalto_1         37  38  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.22   20.23   0.0     \n",
      "Aalto_1         38  39  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.23   20.24   0.0     \n",
      "Aalto_1         39  40  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.23   20.23   0.0     \n",
      "Aalto_1         40  41  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.15   20.15   0.0     \n",
      "Aalto_1         41  42  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.38   20.39   0.0     \n",
      "Aalto_1         42  43  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.21   20.21   0.0     \n",
      "Aalto_1         43  44  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.26   20.26   0.0     \n",
      "Aalto_1         44  45  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.22   20.22   0.0     \n",
      "Aalto_1         45  46  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.21   20.22   0.0     \n",
      "Aalto_1         46  47  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.26   20.26   0.0     \n",
      "Aalto_1         47  48  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.28   20.28   0.0     \n",
      "Aalto_1         48  49  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.2    20.2    0.0     \n",
      "Aalto_1         49  50  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.22   20.22   0.0     \n",
      "Aalto_1         50  51  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.24   20.24   0.0     \n",
      "Aalto_1         51  52  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.22   20.22   0.0     \n",
      "Aalto_1         52  53  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.27   20.28   0.0     \n",
      "Aalto_1         53  54  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.2    20.21   0.0     \n",
      "Aalto_1         54  55  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.21   20.21   0.0     \n",
      "Aalto_1         55  56  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.22   20.23   0.0     \n",
      "Aalto_1         56  57  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.21   20.22   0.0     \n",
      "Aalto_1         57  58  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.29   20.29   0.0     \n",
      "Aalto_1         58  59  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.26   20.26   0.0     \n",
      "Aalto_1         59  60  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.22   20.22   0.0     \n",
      "Aalto_1         60  61  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.21   20.22   0.0     \n",
      "Aalto_1         61  62  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.24   20.25   0.0     \n",
      "Aalto_1         62  63  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.21   20.21   0.0     \n",
      "Aalto_1         63  64  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.22   20.23   0.0     \n",
      "Aalto_1         64  65  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.01     20.26   20.26   0.0     \n",
      "Aalto_1         65  66  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.24   20.24   0.0     \n",
      "Aalto_1         66  67  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.23   20.23   0.0     \n",
      "Aalto_1         67  68  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.19   20.2    0.0     \n",
      "Aalto_1         68  69  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.22   20.22   0.0     \n",
      "Aalto_1         69  70  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.2    20.21   0.0     \n",
      "Aalto_1         70  71  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.31   20.32   0.0     \n",
      "Aalto_1         71  72  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.23   20.23   0.0     \n",
      "Aalto_1         72  73  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.24   20.25   0.0     \n",
      "Aalto_1         73  74  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.2    20.21   0.0     \n",
      "Aalto_1         74  75  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.2    20.21   0.0     \n",
      "Aalto_1         75  76  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.23   20.23   0.0     \n",
      "Aalto_1         76  77  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.3    20.3    0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aalto_1         77  78  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.2    20.2    0.0     \n",
      "Aalto_1         78  79  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.26   20.27   0.0     \n",
      "Aalto_1         79  80  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.19   20.2    0.0     \n",
      "Aalto_1         80  81  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.2    20.2    0.0     \n",
      "Aalto_1         81  82  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.24   20.24   0.0     \n",
      "Aalto_1         82  83  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.01     20.24   20.25   0.0     \n",
      "Aalto_1         83  84  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.27   20.28   0.0     \n",
      "Aalto_1         84  85  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.24   20.25   0.0     \n",
      "Aalto_1         85  86  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.22   20.23   0.0     \n",
      "Aalto_1         86  87  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.01     20.21   20.21   0.0     \n",
      "Aalto_1         87  88  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.51   20.51   0.0     \n",
      "Aalto_1         88  89  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.01     20.37   20.38   0.0     \n",
      "Aalto_1         89  90  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.18   20.18   0.0     \n",
      "Aalto_1         90  91  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.2    20.2    0.0     \n",
      "Aalto_1         91  92  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.15   20.16   0.0     \n",
      "Aalto_1         92  93  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.18   20.18   0.0     \n",
      "Aalto_1         93  94  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.23   20.24   0.0     \n",
      "Aalto_1         94  95  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.17   20.18   0.0     \n",
      "Aalto_1         95  96  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.17   20.18   0.0     \n",
      "Aalto_1         96  97  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.01     20.17   20.17   0.0     \n",
      "Aalto_1         97  98  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.2    20.2    0.0     \n",
      "Aalto_1         98  99  KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.21   20.22   0.0     \n",
      "Aalto_1         99  100 KNN     0.71  0.71  0.75  0.71  0.718    0.68  0.0      20.22   20.23   0.0     \n",
      "                   precision    recall  f1-score       support\n",
      "Aria                0.975309  0.877778  0.923977     90.000000\n",
      "D-LinkCam           0.740053  0.892800  0.809282   1250.000000\n",
      "D-LinkDayCam        0.907609  0.780374  0.839196    214.000000\n",
      "D-LinkDoorSensor    0.787425  0.734637  0.760116    358.000000\n",
      "D-LinkHomeHub       0.710392  0.737618  0.723749   1696.000000\n",
      "D-LinkSensor        0.433333  0.579926  0.496025   1345.000000\n",
      "D-LinkSiren         0.395703  0.357316  0.375531   1237.000000\n",
      "D-LinkSwitch        0.616200  0.616667  0.616433   1320.000000\n",
      "D-LinkWaterSensor   0.571157  0.242156  0.340113   1243.000000\n",
      "EdimaxCam           0.931973  0.830303  0.878205    165.000000\n",
      "EdimaxPlug1101W     0.591241  0.613636  0.602230    264.000000\n",
      "EdimaxPlug2101W     0.356354  0.524390  0.424342    246.000000\n",
      "EdnetCam            0.919355  0.730769  0.814286     78.000000\n",
      "EdnetGateway        0.862069  0.912409  0.886525    137.000000\n",
      "HomeMaticPlug       1.000000  1.000000  1.000000    104.000000\n",
      "HueBridge           0.918800  0.719668  0.807133   2893.000000\n",
      "HueSwitch           0.822765  0.966570  0.888889   3799.000000\n",
      "IKettle2            0.842105  0.571429  0.680851     28.000000\n",
      "Lightify            0.971722  0.976744  0.974227    774.000000\n",
      "MAXGateway          1.000000  0.931034  0.964286    116.000000\n",
      "SmarterCoffee       0.888889  0.571429  0.695652     28.000000\n",
      "TP-LinkPlugHS100    0.614973  0.804196  0.696970    143.000000\n",
      "TP-LinkPlugHS110    0.661017  0.314516  0.426230    124.000000\n",
      "WeMoInsightSwitch   0.687608  0.651182  0.668899   1227.000000\n",
      "WeMoLink            0.590474  0.683816  0.633726   1360.000000\n",
      "WeMoSwitch          0.500721  0.424691  0.459583    810.000000\n",
      "Withings            1.000000  1.000000  1.000000    133.000000\n",
      "accuracy            0.705174  0.705174  0.705174      0.705174\n",
      "macro avg           0.751750  0.705409  0.718017  21182.000000\n",
      "weighted avg        0.709865  0.705174  0.698522  21182.000000\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 2880x2016 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "Aalto_1         0   1   GB      0.66  0.63  0.66  0.63  0.6247   0.63  263.76   0.72    264.48  0.0     \n",
      "Aalto_1         1   2   GB      0.66  0.63  0.66  0.63  0.6247   0.63  263.68   0.73    264.41  0.0     \n",
      "Aalto_1         2   3   GB      0.66  0.63  0.66  0.63  0.6247   0.63  263.71   0.73    264.44  0.0     \n",
      "Aalto_1         3   4   GB      0.66  0.63  0.66  0.63  0.6247   0.63  263.72   0.73    264.45  0.0     \n",
      "Aalto_1         4   5   GB      0.66  0.63  0.66  0.63  0.6247   0.63  263.89   0.73    264.62  0.0     \n",
      "Aalto_1         5   6   GB      0.66  0.63  0.66  0.63  0.6247   0.63  263.57   0.74    264.31  0.0     \n",
      "Aalto_1         6   7   GB      0.66  0.63  0.66  0.63  0.6247   0.63  263.76   0.73    264.49  0.0     \n",
      "Aalto_1         7   8   GB      0.66  0.63  0.66  0.63  0.6247   0.63  264.2    0.73    264.93  0.0     \n",
      "Aalto_1         8   9   GB      0.66  0.63  0.66  0.63  0.6247   0.63  264.78   0.74    265.52  0.0     \n",
      "Aalto_1         9   10  GB      0.66  0.63  0.66  0.63  0.6247   0.63  264.81   0.74    265.55  0.0     \n",
      "                   precision    recall  f1-score       support\n",
      "Aria                0.898876  0.888889  0.893855     90.000000\n",
      "D-LinkCam           0.839301  0.768800  0.802505   1250.000000\n",
      "D-LinkDayCam        0.898990  0.831776  0.864078    214.000000\n",
      "D-LinkDoorSensor    0.715311  0.835196  0.770619    358.000000\n",
      "D-LinkHomeHub       0.450781  0.783019  0.572167   1696.000000\n",
      "D-LinkSensor        0.438040  0.226022  0.298185   1345.000000\n",
      "D-LinkSiren         0.485185  0.211803  0.294879   1237.000000\n",
      "D-LinkSwitch        0.613821  0.571970  0.592157   1320.000000\n",
      "D-LinkWaterSensor   0.353319  0.398230  0.374433   1243.000000\n",
      "EdimaxCam           0.836364  0.836364  0.836364    165.000000\n",
      "EdimaxPlug1101W     0.500000  0.587121  0.540070    264.000000\n",
      "EdimaxPlug2101W     0.654545  0.292683  0.404494    246.000000\n",
      "EdnetCam            0.591837  0.743590  0.659091     78.000000\n",
      "EdnetGateway        0.889831  0.766423  0.823529    137.000000\n",
      "HomeMaticPlug       1.000000  0.480769  0.649351    104.000000\n",
      "HueBridge           0.853679  0.693744  0.765446   2893.000000\n",
      "HueSwitch           0.812556  0.957357  0.879033   3799.000000\n",
      "IKettle2            0.235294  0.142857  0.177778     28.000000\n",
      "Lightify            0.925316  0.944444  0.934783    774.000000\n",
      "MAXGateway          0.658228  0.896552  0.759124    116.000000\n",
      "SmarterCoffee       0.150000  0.428571  0.222222     28.000000\n",
      "TP-LinkPlugHS100    0.601124  0.748252  0.666667    143.000000\n",
      "TP-LinkPlugHS110    0.564103  0.354839  0.435644    124.000000\n",
      "WeMoInsightSwitch   0.729490  0.536267  0.618131   1227.000000\n",
      "WeMoLink            0.581716  0.683088  0.628340   1360.000000\n",
      "WeMoSwitch          0.439525  0.502469  0.468894    810.000000\n",
      "Withings            1.000000  0.879699  0.936000    133.000000\n",
      "accuracy            0.660797  0.660797  0.660797      0.660797\n",
      "macro avg           0.656194  0.629289  0.624735  21182.000000\n",
      "weighted avg        0.669244  0.660797  0.650685  21182.000000\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2880x2016 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test='Aalto_test_IoTDevID.csv'\n",
    "train='Aalto_BIG_train_IoTDevID.csv'\n",
    "\n",
    "\n",
    "dataset=\"./Aalto/\"\n",
    "step=1\n",
    "\n",
    "\n",
    "mixed=True\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": "markdown",
   "metadata": {},
   "source": [
    "# SVM & GB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_list={\"SVM\":SVC(C=10,gamma=1),\n",
    "         \"GB\":GradientBoostingClassifier(learning_rate=0.001,subsample=0.1,n_estimators=500,max_depth= 10,)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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_1         0   1   SVM     0.68  0.63  0.7   0.63  0.6493   0.65  103.6    65.4    169.01  0.0     \n",
      "Aalto_1         1   2   SVM     0.68  0.63  0.7   0.63  0.6493   0.65  102.55   65.57   168.12  0.0     \n",
      "Aalto_1         2   3   SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.72   65.26   166.99  0.0     \n",
      "Aalto_1         3   4   SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.51   64.73   166.24  0.0     \n",
      "Aalto_1         4   5   SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.24   64.84   166.08  0.0     \n",
      "Aalto_1         5   6   SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.17   64.87   166.04  0.0     \n",
      "Aalto_1         6   7   SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.21   64.83   166.04  0.0     \n",
      "Aalto_1         7   8   SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.21   64.78   165.99  0.0     \n",
      "Aalto_1         8   9   SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.12   64.9    166.02  0.0     \n",
      "Aalto_1         9   10  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.1    64.91   166.01  0.0     \n",
      "Aalto_1         10  11  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.13   64.83   165.95  0.0     \n",
      "Aalto_1         11  12  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.19   64.82   166.01  0.0     \n",
      "Aalto_1         12  13  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.26   64.75   166.02  0.0     \n",
      "Aalto_1         13  14  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.2    64.75   165.95  0.0     \n",
      "Aalto_1         14  15  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.27   64.79   166.06  0.0     \n",
      "Aalto_1         15  16  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.12   64.97   166.09  0.0     \n",
      "Aalto_1         16  17  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.16   64.72   165.88  0.0     \n",
      "Aalto_1         17  18  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.22   64.8    166.03  0.0     \n",
      "Aalto_1         18  19  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.11   64.8    165.91  0.0     \n",
      "Aalto_1         19  20  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.28   64.76   166.04  0.0     \n",
      "Aalto_1         20  21  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.17   64.74   165.91  0.0     \n",
      "Aalto_1         21  22  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.37   64.86   166.22  0.0     \n",
      "Aalto_1         22  23  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.14   64.72   165.86  0.0     \n",
      "Aalto_1         23  24  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.3    64.85   166.15  0.0     \n",
      "Aalto_1         24  25  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.18   64.8    165.99  0.0     \n",
      "Aalto_1         25  26  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.12   64.76   165.88  0.0     \n",
      "Aalto_1         26  27  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.15   64.81   165.96  0.0     \n",
      "Aalto_1         27  28  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.18   64.76   165.94  0.0     \n",
      "Aalto_1         28  29  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.07   64.88   165.95  0.0     \n",
      "Aalto_1         29  30  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.08   64.63   165.71  0.0     \n",
      "Aalto_1         30  31  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.36   64.76   166.12  0.0     \n",
      "Aalto_1         31  32  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.23   64.81   166.04  0.0     \n",
      "Aalto_1         32  33  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.25   64.7    165.95  0.0     \n",
      "Aalto_1         33  34  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.25   64.81   166.06  0.0     \n",
      "Aalto_1         34  35  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.33   64.65   165.98  0.0     \n",
      "Aalto_1         35  36  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.29   64.76   166.05  0.0     \n",
      "Aalto_1         36  37  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.19   64.83   166.02  0.0     \n",
      "Aalto_1         37  38  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.42   64.95   166.37  0.0     \n",
      "Aalto_1         38  39  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.24   64.72   165.96  0.0     \n",
      "Aalto_1         39  40  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.25   64.7    165.95  0.0     \n",
      "Aalto_1         40  41  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.33   64.72   166.05  0.0     \n",
      "Aalto_1         41  42  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.43   64.78   166.21  0.0     \n",
      "Aalto_1         42  43  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.2    64.7    165.9   0.0     \n",
      "Aalto_1         43  44  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.37   64.68   166.04  0.0     \n",
      "Aalto_1         44  45  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  102.55   64.87   167.42  0.0     \n",
      "Aalto_1         45  46  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.35   64.81   166.16  0.0     \n",
      "Aalto_1         46  47  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.14   64.88   166.02  0.0     \n",
      "Aalto_1         47  48  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.18   64.75   165.93  0.0     \n",
      "Aalto_1         48  49  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.38   64.69   166.07  0.0     \n",
      "Aalto_1         49  50  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.3    64.71   166.01  0.0     \n",
      "Aalto_1         50  51  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.31   64.72   166.04  0.0     \n",
      "Aalto_1         51  52  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.34   64.75   166.09  0.0     \n",
      "Aalto_1         52  53  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.48   64.81   166.29  0.0     \n",
      "Aalto_1         53  54  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.35   64.81   166.17  0.0     \n",
      "Aalto_1         54  55  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.29   64.82   166.1   0.0     \n",
      "Aalto_1         55  56  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.24   64.76   166.0   0.0     \n",
      "Aalto_1         56  57  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.27   64.68   165.95  0.0     \n",
      "Aalto_1         57  58  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.32   64.63   165.95  0.0     \n",
      "Aalto_1         58  59  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.25   64.72   165.97  0.0     \n",
      "Aalto_1         59  60  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.31   64.81   166.12  0.0     \n",
      "Aalto_1         60  61  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.15   64.74   165.89  0.0     \n",
      "Aalto_1         61  62  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.29   64.69   165.98  0.0     \n",
      "Aalto_1         62  63  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.13   65.09   166.22  0.0     \n",
      "Aalto_1         63  64  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.24   64.74   165.98  0.0     \n",
      "Aalto_1         64  65  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.35   64.97   166.31  0.0     \n",
      "Aalto_1         65  66  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.27   64.75   166.01  0.0     \n",
      "Aalto_1         66  67  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.32   64.7    166.02  0.0     \n",
      "Aalto_1         67  68  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.1    64.82   165.92  0.0     \n",
      "Aalto_1         68  69  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.28   64.82   166.1   0.0     \n",
      "Aalto_1         69  70  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.22   64.77   165.99  0.0     \n",
      "Aalto_1         70  71  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.25   64.76   166.01  0.0     \n",
      "Aalto_1         71  72  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.19   64.9    166.09  0.0     \n",
      "Aalto_1         72  73  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.2    64.79   165.99  0.0     \n",
      "Aalto_1         73  74  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.2    64.76   165.96  0.0     \n",
      "Aalto_1         74  75  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.19   64.81   166.0   0.0     \n",
      "Aalto_1         75  76  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.29   64.7    165.99  0.0     \n",
      "Aalto_1         76  77  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.24   64.69   165.92  0.0     \n",
      "Aalto_1         77  78  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.29   64.71   166.0   0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aalto_1         78  79  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.25   64.78   166.02  0.0     \n",
      "Aalto_1         79  80  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.27   64.76   166.04  0.0     \n",
      "Aalto_1         80  81  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.21   64.86   166.07  0.0     \n",
      "Aalto_1         81  82  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.13   64.72   165.85  0.0     \n",
      "Aalto_1         82  83  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.13   64.79   165.92  0.0     \n",
      "Aalto_1         83  84  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.15   64.73   165.87  0.0     \n",
      "Aalto_1         84  85  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.3    64.71   166.01  0.0     \n",
      "Aalto_1         85  86  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.15   64.85   166.01  0.0     \n",
      "Aalto_1         86  87  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.4    64.73   166.13  0.0     \n",
      "Aalto_1         87  88  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.34   64.71   166.05  0.0     \n",
      "Aalto_1         88  89  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.2    64.72   165.92  0.0     \n",
      "Aalto_1         89  90  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.26   64.82   166.08  0.0     \n",
      "Aalto_1         90  91  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.18   64.85   166.03  0.0     \n",
      "Aalto_1         91  92  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.28   64.76   166.05  0.0     \n",
      "Aalto_1         92  93  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.26   64.77   166.04  0.0     \n",
      "Aalto_1         93  94  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.36   64.75   166.11  0.0     \n",
      "Aalto_1         94  95  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.2    64.72   165.92  0.0     \n",
      "Aalto_1         95  96  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.21   64.71   165.92  0.0     \n",
      "Aalto_1         96  97  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.27   64.72   165.99  0.0     \n",
      "Aalto_1         97  98  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.3    64.71   166.01  0.0     \n",
      "Aalto_1         98  99  SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.3    64.84   166.14  0.0     \n",
      "Aalto_1         99  100 SVM     0.68  0.63  0.7   0.63  0.6493   0.65  101.26   64.81   166.07  0.0     \n",
      "Dataset         T   CV  ML alg  Acc   b_Acc Prec  Rec   F1       kap   tra-T    test-T  total   al-time \n",
      "Aalto_1         0   1   GB      0.7   0.69  0.79  0.69  0.723    0.67  966.82   8.18    974.99  0.0     \n",
      "Aalto_1         1   2   GB      0.7   0.69  0.78  0.69  0.724    0.67  968.62   8.2     976.82  0.0     \n",
      "Aalto_1         2   3   GB      0.7   0.69  0.79  0.69  0.7263   0.67  964.6    8.15    972.75  0.0     \n",
      "Aalto_1         3   4   GB      0.7   0.69  0.79  0.69  0.7275   0.67  966.16   8.18    974.34  0.0     \n",
      "Aalto_1         4   5   GB      0.7   0.69  0.79  0.69  0.7252   0.67  964.56   8.2     972.76  0.0     \n",
      "Aalto_1         5   6   GB      0.7   0.69  0.79  0.69  0.7244   0.67  965.07   8.2     973.27  0.0     \n",
      "Aalto_1         6   7   GB      0.7   0.69  0.79  0.69  0.7247   0.67  969.4    9.52    978.92  0.0     \n",
      "Aalto_1         7   8   GB      0.7   0.69  0.79  0.69  0.7238   0.67  966.22   8.23    974.45  0.0     \n",
      "Aalto_1         8   9   GB      0.7   0.69  0.79  0.69  0.725    0.67  963.95   8.32    972.28  0.0     \n",
      "Aalto_1         9   10  GB      0.7   0.69  0.79  0.69  0.7232   0.67  964.07   8.17    972.24  0.0     \n",
      "Aalto_1         10  11  GB      0.7   0.69  0.79  0.69  0.7259   0.67  964.75   8.3     973.05  0.0     \n",
      "Aalto_1         11  12  GB      0.7   0.69  0.79  0.69  0.7262   0.67  964.02   8.22    972.24  0.0     \n",
      "Aalto_1         12  13  GB      0.7   0.69  0.78  0.69  0.7234   0.67  962.67   8.14    970.81  0.0     \n",
      "Aalto_1         13  14  GB      0.7   0.69  0.79  0.69  0.7258   0.67  964.55   8.17    972.72  0.0     \n",
      "Aalto_1         14  15  GB      0.7   0.69  0.79  0.69  0.7254   0.67  964.92   8.21    973.14  0.0     \n",
      "Aalto_1         15  16  GB      0.7   0.69  0.79  0.69  0.7248   0.67  964.63   8.18    972.81  0.0     \n",
      "Aalto_1         16  17  GB      0.7   0.69  0.79  0.69  0.7246   0.67  963.7    8.21    971.91  0.0     \n",
      "Aalto_1         17  18  GB      0.7   0.7   0.79  0.7   0.7265   0.67  965.69   8.19    973.88  0.0     \n",
      "Aalto_1         18  19  GB      0.7   0.69  0.79  0.69  0.7243   0.67  965.98   8.35    974.34  0.0     \n",
      "Aalto_1         19  20  GB      0.7   0.69  0.79  0.69  0.7259   0.67  966.05   8.21    974.26  0.0     \n",
      "Aalto_1         20  21  GB      0.7   0.69  0.79  0.69  0.7236   0.67  965.1    8.2     973.31  0.0     \n",
      "Aalto_1         21  22  GB      0.7   0.69  0.79  0.69  0.7233   0.67  965.43   8.21    973.63  0.0     \n",
      "Aalto_1         22  23  GB      0.7   0.7   0.79  0.7   0.7241   0.67  965.55   8.17    973.72  0.0     \n",
      "Aalto_1         23  24  GB      0.7   0.69  0.79  0.69  0.7264   0.67  964.65   8.2     972.86  0.0     \n"
     ]
    }
   ],
   "source": [
    "test='Aalto_test_IoTDevID.csv'\n",
    "train='Aalto_BIG_train_IoTDevID.csv'\n",
    "\n",
    "\n",
    "\n",
    "dataset=\"./Aalto/\"\n",
    "step=1\n",
    "\n",
    "\n",
    "mixed=False\n",
    "sayac=2\n",
    "output_csv=dataset+str(sayac)+\"_\"+str(step)+\"_\"+str(mixed)+\"100_svm_gb.csv\"\n",
    "target_names=target_name(test)\n",
    "ML(train,test,output_csv,feature,step,mixed,dataset[2:-1]+\"_\"+str(step))   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_list={\"GB\":GradientBoostingClassifier(learning_rate=0.001,subsample=0.1,n_estimators=500,max_depth= 10)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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_1         0   1   GB      0.7   0.69  0.79  0.69  0.7241   0.67  923.34   8.34    931.68  0.0     \n",
      "Aalto_1         1   2   GB      0.7   0.69  0.79  0.69  0.7233   0.67  917.76   8.25    926.0   0.0     \n",
      "Aalto_1         2   3   GB      0.7   0.7   0.79  0.7   0.7272   0.67  915.99   8.33    924.32  0.0     \n",
      "Aalto_1         3   4   GB      0.7   0.69  0.79  0.69  0.7245   0.67  916.56   8.28    924.83  0.0     \n",
      "Aalto_1         4   5   GB      0.7   0.69  0.79  0.69  0.7243   0.67  917.73   8.24    925.97  0.0     \n",
      "Aalto_1         5   6   GB      0.7   0.69  0.79  0.69  0.7228   0.67  917.36   8.31    925.67  0.0     \n",
      "Aalto_1         6   7   GB      0.7   0.69  0.79  0.69  0.7253   0.67  915.47   8.26    923.73  0.0     \n",
      "Aalto_1         7   8   GB      0.7   0.69  0.79  0.69  0.7259   0.67  918.54   8.32    926.86  0.0     \n",
      "Aalto_1         8   9   GB      0.7   0.69  0.79  0.69  0.7249   0.67  920.88   8.37    929.26  0.0     \n",
      "Aalto_1         9   10  GB      0.7   0.69  0.79  0.69  0.7251   0.67  914.71   8.29    922.99  0.0     \n",
      "Aalto_1         10  11  GB      0.7   0.69  0.78  0.69  0.7217   0.67  914.39   8.28    922.68  0.0     \n",
      "Aalto_1         11  12  GB      0.7   0.69  0.79  0.69  0.7251   0.67  915.72   8.25    923.96  0.0     \n",
      "Aalto_1         12  13  GB      0.7   0.7   0.79  0.7   0.7261   0.67  913.51   8.38    921.88  0.0     \n",
      "Aalto_1         13  14  GB      0.7   0.69  0.79  0.69  0.724    0.67  914.44   8.38    922.82  0.0     \n",
      "Aalto_1         14  15  GB      0.7   0.69  0.79  0.69  0.7234   0.67  916.5    8.24    924.74  0.0     \n",
      "Aalto_1         15  16  GB      0.7   0.69  0.79  0.69  0.7254   0.67  917.13   8.38    925.51  0.0     \n",
      "Aalto_1         16  17  GB      0.7   0.7   0.79  0.7   0.726    0.67  914.11   8.41    922.52  0.0     \n",
      "Aalto_1         17  18  GB      0.7   0.69  0.79  0.69  0.7241   0.67  919.32   8.15    927.47  0.0     \n",
      "Aalto_1         18  19  GB      0.7   0.69  0.79  0.69  0.7255   0.67  904.46   8.13    912.59  0.0     \n",
      "Aalto_1         19  20  GB      0.7   0.69  0.79  0.69  0.7253   0.67  944.14   8.57    952.71  0.0     \n",
      "Aalto_1         20  21  GB      0.7   0.69  0.79  0.69  0.7248   0.67  983.77   9.51    993.28  0.0     \n",
      "Aalto_1         21  22  GB      0.7   0.69  0.79  0.69  0.7242   0.67  1029.84  8.53    1038.36 0.0     \n",
      "Aalto_1         22  23  GB      0.7   0.69  0.79  0.69  0.7253   0.67  987.7    9.57    997.27  0.0     \n",
      "Aalto_1         23  24  GB      0.7   0.69  0.79  0.69  0.725    0.67  961.4    8.67    970.07  0.0     \n",
      "Aalto_1         24  25  GB      0.7   0.69  0.79  0.69  0.723    0.67  976.69   8.83    985.52  0.0     \n",
      "Aalto_1         25  26  GB      0.7   0.69  0.78  0.69  0.7223   0.67  956.31   8.2     964.51  0.0     \n",
      "Aalto_1         26  27  GB      0.7   0.69  0.79  0.69  0.7242   0.67  952.38   8.86    961.23  0.0     \n",
      "Aalto_1         27  28  GB      0.7   0.69  0.79  0.69  0.7255   0.67  962.33   9.14    971.47  0.0     \n",
      "Aalto_1         28  29  GB      0.7   0.69  0.78  0.69  0.7241   0.67  912.52   8.21    920.73  0.0     \n",
      "Aalto_1         29  30  GB      0.7   0.69  0.79  0.69  0.7236   0.67  909.97   8.2     918.18  0.0     \n",
      "Aalto_1         30  31  GB      0.7   0.69  0.79  0.69  0.7254   0.67  936.99   8.27    945.25  0.0     \n",
      "Aalto_1         31  32  GB      0.7   0.69  0.79  0.69  0.7265   0.67  908.89   8.28    917.16  0.0     \n",
      "Aalto_1         32  33  GB      0.7   0.69  0.79  0.69  0.7254   0.67  918.79   8.32    927.11  0.0     \n",
      "Aalto_1         33  34  GB      0.7   0.69  0.79  0.69  0.7246   0.67  907.43   8.29    915.72  0.0     \n",
      "Aalto_1         34  35  GB      0.7   0.69  0.79  0.69  0.7254   0.67  907.88   8.24    916.11  0.0     \n",
      "Aalto_1         35  36  GB      0.7   0.69  0.79  0.69  0.7252   0.67  904.42   8.24    912.65  0.0     \n",
      "Aalto_1         36  37  GB      0.7   0.69  0.79  0.69  0.7257   0.67  905.0    8.26    913.26  0.0     \n",
      "Aalto_1         37  38  GB      0.7   0.7   0.79  0.7   0.7264   0.67  905.15   8.18    913.33  0.0     \n",
      "Aalto_1         38  39  GB      0.7   0.69  0.79  0.69  0.7245   0.67  904.64   8.15    912.79  0.0     \n",
      "Aalto_1         39  40  GB      0.7   0.69  0.79  0.69  0.7222   0.67  904.81   8.19    912.99  0.0     \n",
      "Aalto_1         40  41  GB      0.7   0.69  0.79  0.69  0.7263   0.67  904.52   8.16    912.68  0.0     \n",
      "Aalto_1         41  42  GB      0.7   0.69  0.79  0.69  0.7243   0.67  904.04   8.15    912.19  0.0     \n",
      "Aalto_1         42  43  GB      0.7   0.69  0.79  0.69  0.723    0.67  905.04   8.3     913.34  0.0     \n",
      "Aalto_1         43  44  GB      0.7   0.69  0.79  0.69  0.7238   0.67  905.17   8.2     913.37  0.0     \n",
      "Aalto_1         44  45  GB      0.7   0.69  0.79  0.69  0.7257   0.67  907.0    8.17    915.17  0.0     \n",
      "Aalto_1         45  46  GB      0.7   0.69  0.79  0.69  0.7239   0.67  907.99   8.19    916.17  0.0     \n",
      "Aalto_1         46  47  GB      0.7   0.69  0.79  0.69  0.7239   0.67  908.26   8.18    916.44  0.0     \n",
      "Aalto_1         47  48  GB      0.7   0.69  0.79  0.69  0.7255   0.67  907.89   8.17    916.06  0.0     \n",
      "Aalto_1         48  49  GB      0.7   0.69  0.79  0.69  0.7277   0.67  907.97   8.22    916.19  0.0     \n",
      "Aalto_1         49  50  GB      0.7   0.69  0.78  0.69  0.7229   0.67  907.93   8.17    916.1   0.0     \n",
      "Aalto_1         50  51  GB      0.7   0.69  0.78  0.69  0.7231   0.67  907.57   8.35    915.92  0.0     \n",
      "Aalto_1         51  52  GB      0.7   0.69  0.79  0.69  0.7247   0.67  908.92   8.21    917.13  0.0     \n",
      "Aalto_1         52  53  GB      0.7   0.69  0.79  0.69  0.7253   0.67  907.91   8.18    916.09  0.0     \n",
      "Aalto_1         53  54  GB      0.7   0.69  0.79  0.69  0.7256   0.67  907.84   8.19    916.03  0.0     \n",
      "Aalto_1         54  55  GB      0.7   0.69  0.79  0.69  0.727    0.67  907.41   8.19    915.6   0.0     \n",
      "Aalto_1         55  56  GB      0.7   0.69  0.79  0.69  0.7255   0.67  912.35   8.27    920.62  0.0     \n",
      "Aalto_1         56  57  GB      0.7   0.69  0.78  0.69  0.7238   0.67  915.13   8.34    923.47  0.0     \n",
      "Aalto_1         57  58  GB      0.7   0.69  0.79  0.69  0.7242   0.67  917.31   8.31    925.62  0.0     \n",
      "Aalto_1         58  59  GB      0.7   0.69  0.79  0.69  0.7246   0.67  916.34   8.35    924.69  0.0     \n",
      "Aalto_1         59  60  GB      0.7   0.69  0.79  0.69  0.7244   0.67  915.71   8.26    923.97  0.0     \n",
      "Aalto_1         60  61  GB      0.7   0.69  0.79  0.69  0.7254   0.67  925.41   8.29    933.7   0.0     \n",
      "Aalto_1         61  62  GB      0.7   0.7   0.79  0.7   0.7248   0.67  914.97   8.24    923.22  0.0     \n",
      "Aalto_1         62  63  GB      0.7   0.69  0.79  0.69  0.7219   0.67  915.62   8.35    923.97  0.0     \n",
      "Aalto_1         63  64  GB      0.7   0.69  0.79  0.69  0.7247   0.67  916.5    8.29    924.79  0.0     \n",
      "Aalto_1         64  65  GB      0.7   0.69  0.79  0.69  0.7239   0.67  916.35   8.24    924.59  0.0     \n",
      "Aalto_1         65  66  GB      0.7   0.69  0.78  0.69  0.7236   0.67  916.84   8.25    925.09  0.0     \n",
      "Aalto_1         66  67  GB      0.7   0.69  0.79  0.69  0.7237   0.67  917.4    8.24    925.65  0.0     \n",
      "Aalto_1         67  68  GB      0.7   0.69  0.79  0.69  0.7249   0.67  914.41   8.35    922.76  0.0     \n",
      "Aalto_1         68  69  GB      0.7   0.69  0.79  0.69  0.7252   0.67  916.77   8.27    925.05  0.0     \n",
      "Aalto_1         69  70  GB      0.7   0.69  0.79  0.69  0.7253   0.67  914.77   8.27    923.05  0.0     \n",
      "Aalto_1         70  71  GB      0.7   0.69  0.79  0.69  0.7237   0.67  914.53   8.26    922.79  0.0     \n",
      "Aalto_1         71  72  GB      0.7   0.69  0.79  0.69  0.7257   0.67  914.11   8.28    922.39  0.0     \n",
      "Aalto_1         72  73  GB      0.7   0.69  0.79  0.69  0.7257   0.67  914.4    8.25    922.65  0.0     \n",
      "Aalto_1         73  74  GB      0.7   0.69  0.78  0.69  0.7223   0.67  916.46   8.27    924.74  0.0     \n",
      "Aalto_1         74  75  GB      0.7   0.69  0.79  0.69  0.7258   0.67  916.8    8.29    925.09  0.0     \n",
      "Aalto_1         75  76  GB      0.7   0.69  0.79  0.69  0.7256   0.67  925.42   8.28    933.7   0.0     \n",
      "Aalto_1         76  77  GB      0.7   0.69  0.79  0.69  0.7242   0.67  916.91   8.25    925.16  0.0     \n",
      "Aalto_1         77  78  GB      0.7   0.69  0.79  0.69  0.7265   0.67  914.08   8.23    922.31  0.0     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aalto_1         78  79  GB      0.7   0.69  0.79  0.69  0.7257   0.67  914.59   8.25    922.84  0.0     \n",
      "Aalto_1         79  80  GB      0.7   0.69  0.79  0.69  0.7263   0.67  916.05   8.24    924.29  0.0     \n",
      "Aalto_1         80  81  GB      0.7   0.69  0.79  0.69  0.7255   0.67  915.67   8.26    923.93  0.0     \n",
      "Aalto_1         81  82  GB      0.7   0.69  0.79  0.69  0.724    0.67  914.74   8.27    923.02  0.0     \n",
      "Aalto_1         82  83  GB      0.7   0.69  0.79  0.69  0.7248   0.67  915.87   8.27    924.14  0.0     \n",
      "Aalto_1         83  84  GB      0.7   0.69  0.79  0.69  0.7235   0.67  916.95   8.41    925.36  0.0     \n",
      "Aalto_1         84  85  GB      0.7   0.69  0.79  0.69  0.7236   0.67  916.0    8.24    924.23  0.0     \n",
      "Aalto_1         85  86  GB      0.7   0.69  0.79  0.69  0.7248   0.67  916.21   8.28    924.48  0.0     \n",
      "Aalto_1         86  87  GB      0.7   0.69  0.79  0.69  0.7244   0.67  916.2    8.37    924.57  0.0     \n",
      "Aalto_1         87  88  GB      0.7   0.69  0.79  0.69  0.7243   0.67  914.82   8.35    923.17  0.0     \n",
      "Aalto_1         88  89  GB      0.7   0.69  0.79  0.69  0.7251   0.67  910.43   8.21    918.64  0.0     \n",
      "Aalto_1         89  90  GB      0.7   0.69  0.79  0.69  0.7263   0.67  908.83   8.2     917.02  0.0     \n",
      "Aalto_1         90  91  GB      0.7   0.69  0.79  0.69  0.7252   0.67  909.81   8.18    917.99  0.0     \n",
      "Aalto_1         91  92  GB      0.7   0.7   0.79  0.7   0.7256   0.67  907.14   8.25    915.38  0.0     \n",
      "Aalto_1         92  93  GB      0.7   0.69  0.79  0.69  0.7242   0.67  907.27   8.15    915.42  0.0     \n",
      "Aalto_1         93  94  GB      0.7   0.69  0.79  0.69  0.7254   0.67  909.04   8.18    917.22  0.0     \n",
      "Aalto_1         94  95  GB      0.7   0.69  0.79  0.69  0.7242   0.67  908.62   8.21    916.83  0.0     \n",
      "Aalto_1         95  96  GB      0.7   0.69  0.79  0.69  0.726    0.67  907.12   8.23    915.35  0.0     \n",
      "Aalto_1         96  97  GB      0.7   0.69  0.79  0.69  0.7258   0.67  906.68   8.29    914.98  0.0     \n",
      "Aalto_1         97  98  GB      0.7   0.69  0.79  0.69  0.725    0.67  906.96   8.3     915.26  0.0     \n",
      "Aalto_1         98  99  GB      0.7   0.69  0.79  0.69  0.7238   0.67  906.75   8.19    914.94  0.0     \n",
      "Aalto_1         99  100 GB      0.7   0.69  0.79  0.69  0.7238   0.67  907.83   8.17    916.0   0.0     \n"
     ]
    }
   ],
   "source": [
    "test='Aalto_test_IoTDevID.csv'\n",
    "train='Aalto_BIG_train_IoTDevID.csv'\n",
    "\n",
    "\n",
    "\n",
    "dataset=\"./Aalto/\"\n",
    "step=1\n",
    "\n",
    "\n",
    "mixed=False\n",
    "sayac=2\n",
    "output_csv=dataset+str(sayac)+\"_\"+str(step)+\"_\"+str(mixed)+\"100_svm_gb_GB.csv\"\n",
    "target_names=target_name(test)\n",
    "ML(train,test,output_csv,feature,step,mixed,dataset[2:-1]+\"_\"+str(step))   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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_1         0   1   MLP     0.59  0.54  0.62  0.54  0.5443   0.55  10750.28 2.02    10752.290.0     \n",
      "Aalto_1         1   2   MLP     0.59  0.54  0.64  0.54  0.5508   0.55  13111.1  2.42    13113.520.0     \n",
      "Aalto_1         2   3   MLP     0.58  0.51  0.57  0.51  0.5174   0.54  7761.6   2.22    7763.82 0.0     \n",
      "Aalto_1         3   4   MLP     0.59  0.53  0.6   0.53  0.5471   0.55  7205.08  2.03    7207.11 0.0     \n",
      "Aalto_1         4   5   MLP     0.57  0.51  0.55  0.51  0.5068   0.53  8718.87  1.97    8720.84 0.0     \n",
      "Aalto_1         5   6   MLP     0.57  0.51  0.59  0.51  0.5087   0.52  5487.83  1.95    5489.78 0.0     \n",
      "Aalto_1         6   7   MLP     0.55  0.51  0.55  0.51  0.4894   0.51  4378.44  1.87    4380.32 0.0     \n",
      "Aalto_1         7   8   MLP     0.57  0.5   0.55  0.5   0.4948   0.53  10942.97 2.14    10945.110.0     \n",
      "Aalto_1         8   9   MLP     0.58  0.51  0.62  0.51  0.5246   0.54  7519.31  1.86    7521.17 0.0     \n",
      "Aalto_1         9   10  MLP     0.54  0.47  0.52  0.47  0.461    0.5   5864.5   2.27    5866.77 0.0     \n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "\n",
    "test='Aalto_test_IoTDevID.csv'\n",
    "train='Aalto_BIG_train_IoTDevID.csv'\n",
    "\n",
    "\n",
    "\n",
    "dataset=\"./Aalto/\"\n",
    "step=1\n",
    "\n",
    "ml_list={\"MLP\":MLPClassifier(solver= 'adam', learning_rate= 'constant', hidden_layer_sizes= (1220, 1965), alpha= 0.1, activation= 'relu')}\n",
    "\n",
    "mixed=False\n",
    "sayac=2\n",
    "output_csv=dataset+str(sayac)+\"_\"+str(step)+\"_\"+str(mixed)+\"100_mlp.csv\"\n",
    "target_names=target_name(test)\n",
    "ML(train,test,output_csv,feature,step,mixed,dataset[2:-1]+\"_\"+str(step))   \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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