4.4 Aalto university results  with combined labels.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This file makes machine learning application for aggregation packets for Aalto University dataset with 4 diffirent group size (3, 6, 9, 12)\n",
    "## In this file, very similar devices are considered as a group in the Aalto University dataset and collected under the same label.\n",
    "### Used machine learning algorithms: RF (Random Forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  importing relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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 RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import KFold\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.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "import csv\n",
    "import math\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from sklearn.metrics import classification_report\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train and test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "loop=\"Aalto__Dataset \"\n",
    "loop2=\"Aalto__Dataset \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ARP</th>\n",
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       "      <th>...</th>\n",
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       "      <th>IP_add_count</th>\n",
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       "      <th>Pck_size</th>\n",
       "      <th>Pck_rawdata</th>\n",
       "      <th>Entropy</th>\n",
       "      <th>Label</th>\n",
       "      <th>Mac</th>\n",
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       "      <td>1</td>\n",
       "      <td>4.126629</td>\n",
       "      <td>Aria</td>\n",
       "      <td>20:f8:5e:ca:91:52</td>\n",
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       "      <td>iKettle2</td>\n",
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       "</table>\n",
       "<p>75000 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ARP  LLC  EAPOL  IP  ICMP  ICMP6  TCP  UDP  TCP_w_size  HTTP  ...  \\\n",
       "0        0    0      1   0     0      0    0    0           0     0  ...   \n",
       "1        0    0      0   1     0      0    1    0        8688     1  ...   \n",
       "2        0    0      0   1     0      0    1    0        9660     1  ...   \n",
       "3        1    0      0   0     0      0    0    0           0     0  ...   \n",
       "4        0    0      0   1     0      0    1    0        9660     1  ...   \n",
       "...    ...  ...    ...  ..   ...    ...  ...  ...         ...   ...  ...   \n",
       "74995    0    0      1   0     0      0    0    0           0     0  ...   \n",
       "74996    0    0      0   1     0      0    0    1           0     0  ...   \n",
       "74997    1    0      0   0     0      0    0    0           0     0  ...   \n",
       "74998    1    0      0   0     0      0    0    0           0     0  ...   \n",
       "74999    0    0      0   1     0      0    0    1           0     0  ...   \n",
       "\n",
       "       IP_padding  IP_add_count  IP_ralert  Portcl_src  Portcl_dst  Pck_size  \\\n",
       "0               0             0          0           0           0        95   \n",
       "1               0             4          0           2           1        60   \n",
       "2               0             4          0           3           1        88   \n",
       "3               0             0          0           0           0         0   \n",
       "4               0             4          0           3           1        88   \n",
       "...           ...           ...        ...         ...         ...       ...   \n",
       "74995           0             0          0           0           0       117   \n",
       "74996           0             2          0           1           1       336   \n",
       "74997           0             0          0           0           0         0   \n",
       "74998           0             0          0           0           0         0   \n",
       "74999           0             2          0           1           1       336   \n",
       "\n",
       "       Pck_rawdata   Entropy     Label                Mac  \n",
       "0                1  1.589209      Aria  20:f8:5e:ca:91:52  \n",
       "1                0  0.000000      Aria  20:f8:5e:ca:91:52  \n",
       "2                1  4.126629      Aria  20:f8:5e:ca:91:52  \n",
       "3                0  0.000000      Aria  20:f8:5e:ca:91:52  \n",
       "4                1  4.126629      Aria  20:f8:5e:ca:91:52  \n",
       "...            ...       ...       ...                ...  \n",
       "74995            1  4.148506  iKettle2  5c:cf:7f:06:d9:02  \n",
       "74996            0  0.000000  iKettle2  5c:cf:7f:06:d9:02  \n",
       "74997            0  0.000000  iKettle2  5c:cf:7f:06:d9:02  \n",
       "74998            0  0.000000  iKettle2  5c:cf:7f:06:d9:02  \n",
       "74999            0  0.000000  iKettle2  5c:cf:7f:06:d9:02  \n",
       "\n",
       "[75000 rows x 27 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(loop)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discovering Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Aria',\n",
       " 'D-LinkCam',\n",
       " 'D-LinkDayCam',\n",
       " 'D-LinkDevice',\n",
       " 'D-LinkSensor',\n",
       " 'D-LinkSiren',\n",
       " 'D-LinkSwitch',\n",
       " 'D-LinkWaterSensor',\n",
       " 'EdimaxCam',\n",
       " 'EdimaxPlug1101W',\n",
       " 'EdimaxPlug2101W',\n",
       " 'EdnetCam',\n",
       " 'EdnetGateway',\n",
       " 'HomeMaticPlug',\n",
       " 'Hue-Device',\n",
       " 'Lightify',\n",
       " 'MAXGateway',\n",
       " 'SmarterCoffee',\n",
       " 'TP-LinkPlugHS100',\n",
       " 'TP-LinkPlugHS110',\n",
       " 'WeMoInsightSwitch',\n",
       " 'WeMoLink',\n",
       " 'WeMoSwitch',\n",
       " 'Withings',\n",
       " 'iKettle2']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def target_names():\n",
    "    name=loop\n",
    "    df = pd.read_csv(name)\n",
    "    target_names=sorted(list(df[df.columns[-2]].unique()))\n",
    "    return target_names\n",
    "target_names=target_names()\n",
    "target_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # new label groups for Aalto dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Aria',\n",
       " 'D-LinkCam',\n",
       " 'D-LinkDayCam',\n",
       " 'D-LinkDevice',\n",
       " 'D-Sensors',\n",
       " 'EdimaxCam',\n",
       " 'EdimaxPlug',\n",
       " 'EdnetCam',\n",
       " 'EdnetGateway',\n",
       " 'HomeMaticPlug',\n",
       " 'Hue-Device',\n",
       " 'Lightify',\n",
       " 'MAXGateway',\n",
       " 'TP-LinkPlugHS',\n",
       " 'WeMoInsightSwitch',\n",
       " 'WeMoLink',\n",
       " 'WeMoSwitch',\n",
       " 'Withings',\n",
       " 'smart_Kettle']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_labels={ 'D-LinkSensor':\"D-Sensors\",\n",
    " 'D-LinkSiren':\"D-Sensors\",\n",
    " 'D-LinkSwitch':\"D-Sensors\",\n",
    " 'D-LinkWaterSensor':\"D-Sensors\",\n",
    " 'EdimaxPlug1101W':\"EdimaxPlug\",\n",
    " 'EdimaxPlug2101W':\"EdimaxPlug\",\n",
    " 'SmarterCoffee':\"smart_Kettle\",\n",
    " 'TP-LinkPlugHS100':\"TP-LinkPlugHS\",\n",
    " 'TP-LinkPlugHS110':\"TP-LinkPlugHS\",\n",
    " 'iKettle2':\"smart_Kettle\"}\n",
    "\n",
    "def target_names():\n",
    "    name=loop\n",
    "    df = pd.read_csv(name)\n",
    "    df=df.replace({\"Label\": new_labels})\n",
    "    target_names=sorted(list(df[df.columns[-2]].unique()))\n",
    "    return target_names\n",
    "target_names=target_names()\n",
    "target_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameters of machine learning algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_list={\"Random Forest R\":RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
    "                       max_depth=27.0, max_features=1, max_leaf_nodes=None,\n",
    "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "                       min_samples_leaf=1, min_samples_split=10,\n",
    "                       min_weight_fraction_leaf=0.0, n_estimators=69,\n",
    "                       n_jobs=None, oob_score=False, random_state=None,\n",
    "                       verbose=0, warm_start=False)}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aggregation Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "altime=0\n",
    "def most_frequent(List): \n",
    "    return max(set(List), key = List.count) \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",
    "\n",
    "\n",
    "def merged(mac,y_test,predict):\n",
    "    step=12\n",
    "    second=time.time()\n",
    "    mac_test=[]\n",
    "    for i in y_test.index:\n",
    "        mac_test.append(mac[i])\n",
    "\n",
    "    d_list=sorted(list(mac.unique()))\n",
    "    devices={}\n",
    "    for i in d_list:\n",
    "        devices[i]=[]    \n",
    "\n",
    "\n",
    "    new_y=[0]*len(y_test)\n",
    "\n",
    "    for i,ii in enumerate (mac_test):\n",
    "        devices[ii].append(i)\n",
    "        #devices[]\n",
    "    for i in devices:\n",
    "        \n",
    "        a = [devices[i][j:j + step] for j in range(0, len(devices[i]), step)]  \n",
    "        for ii in a:\n",
    "            step_list=[]\n",
    "            for iii in ii:\n",
    "                step_list.append(predict[iii])\n",
    "            add=most_frequent(list(step_list))\n",
    "            for iii in ii:\n",
    "                new_y[iii]=add\n",
    "                \n",
    "    return new_y,time.time()-second"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculation of evaluations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def score(altime,train_time,test_time,predict,y_test,class_based_results):\n",
    "    precision=[]\n",
    "    recall=[]\n",
    "    f1=[]\n",
    "    accuracy=[]\n",
    "    total_time=[]\n",
    "    kappa=[]\n",
    "    accuracy_b=[]\n",
    "    \n",
    "    rc=sklearn.metrics.recall_score(y_test, predict,average= \"macro\")\n",
    "    pr=sklearn.metrics.precision_score(y_test, predict,average= \"macro\")\n",
    "    f_1=sklearn.metrics.f1_score(y_test, predict,average= \"macro\")        \n",
    "    report = classification_report(y_test, predict, target_names=target_names,output_dict=True)\n",
    "    cr = pd.DataFrame(report).transpose()\n",
    "    if class_based_results.empty:\n",
    "        class_based_results =cr\n",
    "    else:\n",
    "        class_based_results = class_based_results.add(cr, fill_value=0)\n",
    "    precision.append(float(pr))\n",
    "    recall.append(float(rc))\n",
    "    f1.append(float(f_1))\n",
    "    accuracy_b.append(balanced_accuracy_score( y_test,predict))\n",
    "    accuracy.append(accuracy_score(y_test, predict))\n",
    "    #clf.score(X_test, y_test))\n",
    "    #print(balanced_accuracy_score( y_test,predict))\n",
    "    #t_time.append(float((time.time()-second)) )\n",
    "    kappa.append(round(float(sklearn.metrics.cohen_kappa_score(y_test, predict, \n",
    "    labels=None, weights=None, sample_weight=None)),15))\n",
    "    print ('%-15s %-3s %-3s %-6s  %-5s %-5s %-5s %-5s %-5s %-5s %-8s %-8s%-8s%-8s' % (dname,i,cv,ii[0:6],str(round(np.mean(accuracy),2)),str(round(np.mean(accuracy_b),2)),\n",
    "        str(round(np.mean(precision),2)), str(round(np.mean(recall),2)),str(round(np.mean(f1),2)), \n",
    "        str(round(np.mean(kappa),2)),str(round(np.mean(train_time),2)),str(round(np.mean(test_time),2)),str(round(np.mean(test_time)+np.mean(train_time),2)),str(round(np.mean(altime),2))))\n",
    "    lines=(str(dname)+\",\"+str(i)+\",\"+str(cv)+\",\"+str(ii)+\",\"+str(round(np.mean(accuracy),15))+\",\"+str(round(np.mean(accuracy_b),15))+\",\"+str(round(np.mean(precision),15))+\",\"+ str(round(np.mean(recall),15))+\",\"+str(round(np.mean(f1),15))+\",\"+str(round(np.mean(kappa),15))+\",\"+str(round(np.mean(train_time),15))+\",\"+str(round(np.mean(test_time),15))+\",\"+str(round(np.mean(test_time)+np.mean(train_time),15))+str(altime)+\"\\n\")\n",
    "    return lines,class_based_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine learning applications (100 repetitions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GROUP SIZE 1 (= individual packets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "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__Dataset  0   1   Random  0.88  0.87  0.93  0.87  0.89  0.87  2.42     1.2     3.61    0.0     \n",
      "Aalto__Dataset  1   2   Random  0.88  0.87  0.93  0.87  0.89  0.88  2.43     1.19    3.62    0.0     \n",
      "Aalto__Dataset  2   3   Random  0.88  0.87  0.93  0.87  0.89  0.87  2.34     1.25    3.6     0.0     \n",
      "Aalto__Dataset  3   4   Random  0.88  0.87  0.93  0.87  0.89  0.87  2.34     1.11    3.45    0.0     \n",
      "Aalto__Dataset  4   5   Random  0.88  0.87  0.93  0.87  0.89  0.88  2.13     1.09    3.22    0.0     \n",
      "Aalto__Dataset  5   6   Random  0.88  0.87  0.93  0.87  0.89  0.87  2.23     1.22    3.45    0.0     \n",
      "Aalto__Dataset  6   7   Random  0.88  0.87  0.93  0.87  0.89  0.87  2.18     1.1     3.28    0.0     \n",
      "Aalto__Dataset  7   8   Random  0.88  0.87  0.93  0.87  0.89  0.87  2.42     1.13    3.55    0.0     \n",
      "Aalto__Dataset  8   9   Random  0.88  0.87  0.93  0.87  0.89  0.87  2.38     1.13    3.52    0.0     \n",
      "Aalto__Dataset  9   10  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.14     1.1     3.23    0.0     \n",
      "Aalto__Dataset  10  11  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.42     1.1     3.52    0.0     \n",
      "Aalto__Dataset  11  12  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.17     1.3     3.46    0.0     \n",
      "Aalto__Dataset  12  13  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.14     1.14    3.28    0.0     \n",
      "Aalto__Dataset  13  14  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.16     1.11    3.27    0.0     \n",
      "Aalto__Dataset  14  15  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.56     1.2     3.76    0.0     \n",
      "Aalto__Dataset  15  16  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.4      1.14    3.54    0.0     \n",
      "Aalto__Dataset  16  17  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.19     1.14    3.33    0.0     \n",
      "Aalto__Dataset  17  18  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.15     1.2     3.36    0.0     \n",
      "Aalto__Dataset  18  19  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.13     1.09    3.22    0.0     \n",
      "Aalto__Dataset  19  20  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.07     1.08    3.15    0.0     \n",
      "Aalto__Dataset  20  21  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.1      1.08    3.18    0.0     \n",
      "Aalto__Dataset  21  22  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.13     1.08    3.22    0.0     \n",
      "Aalto__Dataset  22  23  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.12     1.1     3.22    0.0     \n",
      "Aalto__Dataset  23  24  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.31     1.17    3.48    0.0     \n",
      "Aalto__Dataset  24  25  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.18     1.49    3.67    0.0     \n",
      "Aalto__Dataset  25  26  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.84     1.18    4.02    0.0     \n",
      "Aalto__Dataset  26  27  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.46     1.11    3.57    0.0     \n",
      "Aalto__Dataset  27  28  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.46     1.19    3.66    0.0     \n",
      "Aalto__Dataset  28  29  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.21     1.13    3.34    0.0     \n",
      "Aalto__Dataset  29  30  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.79     1.12    3.91    0.0     \n",
      "Aalto__Dataset  30  31  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.17     1.17    3.33    0.0     \n",
      "Aalto__Dataset  31  32  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.17     1.15    3.32    0.0     \n",
      "Aalto__Dataset  32  33  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.75     1.1     3.85    0.0     \n",
      "Aalto__Dataset  33  34  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.75     1.15    3.91    0.0     \n",
      "Aalto__Dataset  34  35  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.09     1.15    3.25    0.0     \n",
      "Aalto__Dataset  35  36  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.2      1.1     3.29    0.0     \n",
      "Aalto__Dataset  36  37  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.4      1.1     3.51    0.0     \n",
      "Aalto__Dataset  37  38  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.19     1.3     3.49    0.0     \n",
      "Aalto__Dataset  38  39  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.15     1.1     3.25    0.0     \n",
      "Aalto__Dataset  39  40  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.39     1.11    3.5     0.0     \n",
      "Aalto__Dataset  40  41  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.42     1.13    3.55    0.0     \n",
      "Aalto__Dataset  41  42  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.16     1.38    3.55    0.0     \n",
      "Aalto__Dataset  42  43  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.2      1.17    3.37    0.0     \n",
      "Aalto__Dataset  43  44  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.19     1.1     3.29    0.0     \n",
      "Aalto__Dataset  44  45  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.31     1.24    3.55    0.0     \n",
      "Aalto__Dataset  45  46  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.15     1.25    3.4     0.0     \n",
      "Aalto__Dataset  46  47  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.43     1.99    4.42    0.0     \n",
      "Aalto__Dataset  47  48  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.8      1.15    3.95    0.0     \n",
      "Aalto__Dataset  48  49  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.3      1.17    3.46    0.0     \n",
      "Aalto__Dataset  49  50  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.32     1.29    3.6     0.0     \n",
      "Aalto__Dataset  50  51  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.69     1.36    4.06    0.0     \n",
      "Aalto__Dataset  51  52  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.38     1.16    3.54    0.0     \n",
      "Aalto__Dataset  52  53  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.32     1.19    3.51    0.0     \n",
      "Aalto__Dataset  53  54  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.35     1.83    4.19    0.0     \n",
      "Aalto__Dataset  54  55  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.13     1.1     3.23    0.0     \n",
      "Aalto__Dataset  55  56  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.4      1.15    3.55    0.0     \n",
      "Aalto__Dataset  56  57  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.16     1.67    3.83    0.0     \n",
      "Aalto__Dataset  57  58  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.46     1.28    3.74    0.0     \n",
      "Aalto__Dataset  58  59  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.5      1.12    3.61    0.0     \n",
      "Aalto__Dataset  59  60  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.18     1.15    3.33    0.0     \n",
      "Aalto__Dataset  60  61  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.47     1.19    3.66    0.0     \n",
      "Aalto__Dataset  61  62  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.59     1.11    3.7     0.0     \n",
      "Aalto__Dataset  62  63  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.21     1.15    3.35    0.0     \n",
      "Aalto__Dataset  63  64  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.41     1.15    3.56    0.0     \n",
      "Aalto__Dataset  64  65  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.49     1.2     3.69    0.0     \n",
      "Aalto__Dataset  65  66  Random  0.88  0.87  0.93  0.87  0.89  0.87  3.17     1.38    4.56    0.0     \n",
      "Aalto__Dataset  66  67  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.38     1.15    3.53    0.0     \n",
      "Aalto__Dataset  67  68  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.33     1.15    3.48    0.0     \n",
      "Aalto__Dataset  68  69  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.22     1.18    3.4     0.0     \n",
      "Aalto__Dataset  69  70  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.93     1.28    4.2     0.0     \n",
      "Aalto__Dataset  70  71  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.25     1.15    3.41    0.0     \n",
      "Aalto__Dataset  71  72  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.45     1.13    3.59    0.0     \n",
      "Aalto__Dataset  72  73  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.12     1.1     3.22    0.0     \n",
      "Aalto__Dataset  73  74  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.1      1.09    3.19    0.0     \n",
      "Aalto__Dataset  74  75  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.08     1.09    3.17    0.0     \n",
      "Aalto__Dataset  75  76  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.07     1.08    3.15    0.0     \n",
      "Aalto__Dataset  76  77  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.08     1.08    3.16    0.0     \n",
      "Aalto__Dataset  77  78  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.08     1.08    3.16    0.0     \n",
      "Aalto__Dataset  78  79  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.1      1.08    3.18    0.0     \n",
      "Aalto__Dataset  79  80  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.11     1.08    3.19    0.0     \n",
      "Aalto__Dataset  80  81  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.05     1.08    3.12    0.0     \n",
      "Aalto__Dataset  81  82  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.07     1.08    3.15    0.0     \n",
      "Aalto__Dataset  82  83  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.12     1.08    3.2     0.0     \n",
      "Aalto__Dataset  83  84  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.07     1.08    3.15    0.0     \n",
      "Aalto__Dataset  84  85  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.1      1.08    3.18    0.0     \n",
      "Aalto__Dataset  85  86  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.09     1.08    3.17    0.0     \n",
      "Aalto__Dataset  86  87  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.08     1.09    3.17    0.0     \n",
      "Aalto__Dataset  87  88  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.08     1.18    3.26    0.0     \n",
      "Aalto__Dataset  88  89  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.11     1.11    3.22    0.0     \n",
      "Aalto__Dataset  89  90  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.11     1.08    3.19    0.0     \n",
      "Aalto__Dataset  90  91  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.06     1.08    3.14    0.0     \n",
      "Aalto__Dataset  91  92  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.1      1.08    3.18    0.0     \n",
      "Aalto__Dataset  92  93  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.07     1.09    3.16    0.0     \n",
      "Aalto__Dataset  93  94  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.07     1.08    3.15    0.0     \n",
      "Aalto__Dataset  94  95  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.06     1.08    3.14    0.0     \n",
      "Aalto__Dataset  95  96  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.13     1.07    3.2     0.0     \n",
      "Aalto__Dataset  96  97  Random  0.88  0.87  0.93  0.87  0.89  0.88  2.07     1.08    3.15    0.0     \n",
      "Aalto__Dataset  97  98  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.06     1.08    3.14    0.0     \n",
      "Aalto__Dataset  98  99  Random  0.88  0.87  0.93  0.87  0.89  0.87  2.06     1.08    3.13    0.0     \n",
      "Aalto__Dataset  99  100 Random  0.88  0.87  0.93  0.87  0.89  0.87  2.08     1.08    3.15    0.0     \n",
      "                   precision    recall  f1-score       support\n",
      "Aria                0.882366  0.757667  0.815275   3000.000000\n",
      "D-LinkCam           0.961251  0.914230  0.937146   3000.000000\n",
      "D-LinkDayCam        0.933348  0.812000  0.868455   3000.000000\n",
      "D-LinkDevice        0.954694  0.769613  0.852210   3000.000000\n",
      "D-Sensors           0.939720  0.931463  0.935572  12000.000000\n",
      "EdimaxCam           0.936135  0.848767  0.890296   3000.000000\n",
      "EdimaxPlug          0.901505  0.851133  0.875594   6000.000000\n",
      "EdnetCam            0.902515  0.873333  0.887684   3000.000000\n",
      "EdnetGateway        0.978199  0.731300  0.836725   3000.000000\n",
      "HomeMaticPlug       1.000000  0.962667  0.980978   3000.000000\n",
      "Hue-Device          0.993437  0.973730  0.983485   3000.000000\n",
      "Lightify            0.994239  0.917563  0.954363   3000.000000\n",
      "MAXGateway          0.986202  0.857667  0.917454   3000.000000\n",
      "TP-LinkPlugHS       0.929723  0.905150  0.917235   6000.000000\n",
      "WeMoInsightSwitch   0.951211  0.888373  0.918715   3000.000000\n",
      "WeMoLink            0.971963  0.917957  0.944186   3000.000000\n",
      "WeMoSwitch          0.951645  0.913213  0.932031   3000.000000\n",
      "Withings            0.998369  0.742867  0.851850   3000.000000\n",
      "smart_Kettle        0.523797  0.988333  0.684710   6000.000000\n",
      "accuracy            0.883841  0.883841  0.883841      0.883841\n",
      "macro avg           0.931069  0.871422  0.893893  75000.000000\n",
      "weighted avg        0.914580  0.883841  0.890729  75000.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ths = open(\"1_08_2020_normal.csv\", \"w\")\n",
    "ths.write(\"Dataset,T,CV,ML algorithm,Acc,b_Acc,Precision, Recall , F1-score, kappa ,tra-Time,test-Time,total-Time),Al-Time)\\n\")\n",
    "repetition=100\n",
    "\n",
    "\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.preprocessing import Normalizer\n",
    "print ('%-15s %-3s %-3s %-6s  %-5s %-5s %-5s %-5s %-5s %-5s %-8s %-8s%-8s%-8s'%\n",
    "           (\"Dataset\",\"T\",\"CV\",\"ML alg\",\"Acc\",\"b_Acc\",\"Prec\", \"Rec\" , \"F1\", \"kap\" ,\"tra-T\",\"test-T\",\"total\",\"al-time\"))\n",
    "\n",
    "for ii in ml_list:\n",
    "    class_based_results=pd.DataFrame()#\"\" #pd.DataFrame(0, index=np.arange((len(target_names)+3)), columns=[\"f1-score\",\"precision\",\"recall\",\"support\"])\n",
    "    cm=pd.DataFrame()\n",
    "    cv=0\n",
    "    for i in range(repetition):\n",
    "\n",
    "\n",
    "        #TRAIN\n",
    "        df = pd.read_csv(loop)\n",
    "        df=df.replace({\"Label\": new_labels})\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)#,header=None )\n",
    "        df=df.replace({\"Label\": new_labels})\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",
    "        dname=loop  \n",
    "        results_y=[]\n",
    "        cv+=1\n",
    "        results_y.append(y_test)\n",
    "\n",
    "\n",
    "        precision=[]\n",
    "        recall=[]\n",
    "        f1=[]\n",
    "        accuracy=[]\n",
    "        train_time=[]\n",
    "        test_time=[]\n",
    "        total_time=[]\n",
    "        kappa=[]\n",
    "        accuracy_b=[]\n",
    "\n",
    "            #machine learning algorithm is applied in this section\n",
    "        clf = ml_list[ii]#choose algorithm from ml_list dictionary\n",
    "        second=time.time()\n",
    "        clf.fit(X_train, y_train)\n",
    "        train_time.append(float((time.time()-second)) )\n",
    "        second=time.time()\n",
    "        predict =clf.predict(X_test)\n",
    "        test_time.append(float((time.time()-second)) )\n",
    "        #normal\n",
    "        lines,class_based_results=score(altime,train_time,test_time,predict,y_test,class_based_results)\n",
    "        #merged\n",
    "        #predict=merged(m_test,y_test,predict)\n",
    "        #lines,class_based_results=score(train_time,test_time,predict,y_test,class_based_results)\n",
    "\n",
    "        ths.write (lines)\n",
    "\n",
    "        df_cm = pd.DataFrame(confusion_matrix(y_test, predict))\n",
    "        results_y.append(predict)\n",
    "        if cm.empty:\n",
    "            cm =df_cm\n",
    "        else:\n",
    "            cm = cm.add(df_cm, fill_value=0)\n",
    "\n",
    "\n",
    "print(class_based_results/(repetition)) \n",
    "graph_name=\"normal_confusion matrix\" +str(ii)+\".pdf\"          \n",
    "plt.figure(figsize = (20,14))\n",
    "sns.heatmap(cm,xticklabels=target_names, yticklabels=target_names, annot=True)\n",
    "plt.savefig(graph_name,bbox_inches='tight')#, dpi=400)\n",
    "plt.show()\n",
    "#print(cm)\n",
    "print(\"\\n\\n\\n\") \n",
    "ths.close()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GROUP SIZE 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "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__Dataset  0   1   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.12     1.08    3.2     0.91    \n",
      "Aalto__Dataset  1   2   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.08     1.09    3.16    1.05    \n",
      "Aalto__Dataset  2   3   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.12     1.08    3.2     1.1     \n",
      "Aalto__Dataset  3   4   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.09     1.08    3.17    1.25    \n",
      "Aalto__Dataset  4   5   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.08     1.08    3.16    1.19    \n",
      "Aalto__Dataset  5   6   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.09     1.08    3.17    0.89    \n",
      "Aalto__Dataset  6   7   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.08     1.08    3.16    1.24    \n",
      "Aalto__Dataset  7   8   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.07     1.08    3.15    1.04    \n",
      "Aalto__Dataset  8   9   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.08     1.08    3.17    1.18    \n",
      "Aalto__Dataset  9   10  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.15     1.17    3.32    1.15    \n",
      "Aalto__Dataset  10  11  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.05     1.08    3.13    1.07    \n",
      "Aalto__Dataset  11  12  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.16     1.12    3.28    1.19    \n",
      "Aalto__Dataset  12  13  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.08     1.08    3.16    1.0     \n",
      "Aalto__Dataset  13  14  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.09     1.19    3.27    1.0     \n",
      "Aalto__Dataset  14  15  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.07     1.1     3.18    1.13    \n",
      "Aalto__Dataset  15  16  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.34     1.18    3.52    1.04    \n",
      "Aalto__Dataset  16  17  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.11     1.18    3.29    0.87    \n",
      "Aalto__Dataset  17  18  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.21     1.12    3.34    1.14    \n",
      "Aalto__Dataset  18  19  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.06     1.08    3.14    1.25    \n",
      "Aalto__Dataset  19  20  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.1      1.14    3.24    1.02    \n",
      "Aalto__Dataset  20  21  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.36     1.16    3.52    0.94    \n",
      "Aalto__Dataset  21  22  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.19     1.19    3.38    0.92    \n",
      "Aalto__Dataset  22  23  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.07     1.08    3.15    1.06    \n",
      "Aalto__Dataset  23  24  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.19     1.13    3.32    0.94    \n",
      "Aalto__Dataset  24  25  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.07     1.08    3.15    1.12    \n",
      "Aalto__Dataset  25  26  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.4      1.17    3.57    0.95    \n",
      "Aalto__Dataset  26  27  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.09     1.08    3.17    1.02    \n",
      "Aalto__Dataset  27  28  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.26     1.12    3.38    0.86    \n",
      "Aalto__Dataset  28  29  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.23     1.12    3.34    0.85    \n",
      "Aalto__Dataset  29  30  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.18     1.08    3.26    1.16    \n",
      "Aalto__Dataset  30  31  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.07     1.08    3.15    0.95    \n",
      "Aalto__Dataset  31  32  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.07     1.08    3.15    0.91    \n",
      "Aalto__Dataset  32  33  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.06     1.08    3.14    1.08    \n",
      "Aalto__Dataset  33  34  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.08     1.08    3.16    0.99    \n",
      "Aalto__Dataset  34  35  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.19     1.09    3.28    1.21    \n",
      "Aalto__Dataset  35  36  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.12     1.16    3.28    0.92    \n",
      "Aalto__Dataset  36  37  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.1      1.09    3.19    1.02    \n",
      "Aalto__Dataset  37  38  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.13     1.12    3.25    1.0     \n",
      "Aalto__Dataset  38  39  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.1      1.11    3.21    0.89    \n",
      "Aalto__Dataset  39  40  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.22     1.21    3.43    1.02    \n",
      "Aalto__Dataset  40  41  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.07     1.09    3.16    0.9     \n",
      "Aalto__Dataset  41  42  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.18     1.12    3.31    0.95    \n",
      "Aalto__Dataset  42  43  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.18     1.11    3.3     0.9     \n",
      "Aalto__Dataset  43  44  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.28     1.22    3.5     1.03    \n",
      "Aalto__Dataset  44  45  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.75     1.14    3.9     1.04    \n",
      "Aalto__Dataset  45  46  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.34     1.2     3.54    1.28    \n",
      "Aalto__Dataset  46  47  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.19     1.22    3.41    1.12    \n",
      "Aalto__Dataset  47  48  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.23     1.2     3.43    1.23    \n",
      "Aalto__Dataset  48  49  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.53     1.21    3.73    1.2     \n",
      "Aalto__Dataset  49  50  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.23     1.17    3.4     1.27    \n",
      "Aalto__Dataset  50  51  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.21     1.14    3.36    1.07    \n",
      "Aalto__Dataset  51  52  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.24     1.22    3.46    1.22    \n",
      "Aalto__Dataset  52  53  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.14     1.14    3.28    1.08    \n",
      "Aalto__Dataset  53  54  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.21     1.21    3.41    0.95    \n",
      "Aalto__Dataset  54  55  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.13     1.13    3.26    1.01    \n",
      "Aalto__Dataset  55  56  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.14     1.14    3.29    0.95    \n",
      "Aalto__Dataset  56  57  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.32     1.17    3.49    1.02    \n",
      "Aalto__Dataset  57  58  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.22     1.17    3.38    1.09    \n",
      "Aalto__Dataset  58  59  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.27     1.21    3.49    0.97    \n",
      "Aalto__Dataset  59  60  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.19     1.19    3.38    0.94    \n",
      "Aalto__Dataset  60  61  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.31     1.12    3.43    1.05    \n",
      "Aalto__Dataset  61  62  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.13     1.15    3.28    0.93    \n",
      "Aalto__Dataset  62  63  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.21     1.13    3.34    0.9     \n",
      "Aalto__Dataset  63  64  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.4      1.44    3.84    1.08    \n",
      "Aalto__Dataset  64  65  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.4      1.46    3.86    1.49    \n",
      "Aalto__Dataset  65  66  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.46     1.32    3.78    1.13    \n",
      "Aalto__Dataset  66  67  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.19     1.16    3.35    1.02    \n",
      "Aalto__Dataset  67  68  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.22     1.44    3.66    1.36    \n",
      "Aalto__Dataset  68  69  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.84     1.68    4.52    1.23    \n",
      "Aalto__Dataset  69  70  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.44     1.19    3.64    1.1     \n",
      "Aalto__Dataset  70  71  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.3      1.16    3.46    1.04    \n",
      "Aalto__Dataset  71  72  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.18     1.26    3.44    1.56    \n",
      "Aalto__Dataset  72  73  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.19     1.18    3.37    1.03    \n",
      "Aalto__Dataset  73  74  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.52     1.17    3.69    0.89    \n",
      "Aalto__Dataset  74  75  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.09     1.15    3.24    1.02    \n",
      "Aalto__Dataset  75  76  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.34     1.13    3.47    1.04    \n",
      "Aalto__Dataset  76  77  Random  0.99  0.99  0.99  0.99  0.99  0.99  3.22     1.21    4.44    0.99    \n",
      "Aalto__Dataset  77  78  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.12     1.11    3.23    1.28    \n",
      "Aalto__Dataset  78  79  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.31     1.29    3.6     2.37    \n",
      "Aalto__Dataset  79  80  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.99     1.12    4.11    1.03    \n",
      "Aalto__Dataset  80  81  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.31     1.18    3.49    1.37    \n",
      "Aalto__Dataset  81  82  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.65     1.12    3.78    0.93    \n",
      "Aalto__Dataset  82  83  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.33     1.21    3.54    1.14    \n",
      "Aalto__Dataset  83  84  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.21     1.14    3.35    0.97    \n",
      "Aalto__Dataset  84  85  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.43     1.18    3.61    1.05    \n",
      "Aalto__Dataset  85  86  Random  0.99  0.99  0.99  0.99  0.99  0.99  3.09     1.5     4.59    1.27    \n",
      "Aalto__Dataset  86  87  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.18     1.16    3.34    0.92    \n",
      "Aalto__Dataset  87  88  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.19     1.1     3.29    1.08    \n",
      "Aalto__Dataset  88  89  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.27     1.16    3.44    1.31    \n",
      "Aalto__Dataset  89  90  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.33     1.14    3.47    0.9     \n",
      "Aalto__Dataset  90  91  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.31     1.74    4.05    1.28    \n",
      "Aalto__Dataset  91  92  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.22     1.1     3.31    1.04    \n",
      "Aalto__Dataset  92  93  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.07     1.1     3.17    0.92    \n",
      "Aalto__Dataset  93  94  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.08     1.1     3.18    0.97    \n",
      "Aalto__Dataset  94  95  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.14     1.11    3.25    0.91    \n",
      "Aalto__Dataset  95  96  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.2      1.12    3.33    1.0     \n",
      "Aalto__Dataset  96  97  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.1      1.09    3.19    0.91    \n",
      "Aalto__Dataset  97  98  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.12     1.11    3.23    0.91    \n",
      "Aalto__Dataset  98  99  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.13     1.11    3.24    1.05    \n",
      "Aalto__Dataset  99  100 Random  0.99  0.99  0.99  0.99  0.99  0.99  2.11     1.12    3.22    0.97    \n",
      "                   precision    recall  f1-score       support\n",
      "Aria                0.994924  0.980000  0.987406   3000.000000\n",
      "D-LinkCam           0.997556  1.000000  0.998777   3000.000000\n",
      "D-LinkDayCam        0.997663  0.996000  0.996831   3000.000000\n",
      "D-LinkDevice        0.999119  0.998360  0.998739   3000.000000\n",
      "D-Sensors           0.998047  1.000000  0.999022  12000.000000\n",
      "EdimaxCam           1.000000  0.999333  0.999667   3000.000000\n",
      "EdimaxPlug          0.992184  0.998000  0.995084   6000.000000\n",
      "EdnetCam            0.990426  1.000000  0.995190   3000.000000\n",
      "EdnetGateway        0.999815  0.980880  0.990255   3000.000000\n",
      "HomeMaticPlug       1.000000  1.000000  1.000000   3000.000000\n",
      "Hue-Device          0.999594  1.000000  0.999797   3000.000000\n",
      "Lightify            1.000000  1.000000  1.000000   3000.000000\n",
      "MAXGateway          0.999667  1.000000  0.999833   3000.000000\n",
      "TP-LinkPlugHS       0.992793  0.998000  0.995390   6000.000000\n",
      "WeMoInsightSwitch   0.986403  0.947847  0.966740   3000.000000\n",
      "WeMoLink            0.988088  1.000000  0.994008   3000.000000\n",
      "WeMoSwitch          0.988808  0.927870  0.957370   3000.000000\n",
      "Withings            1.000000  0.996000  0.997996   3000.000000\n",
      "smart_Kettle        0.958413  1.000000  0.978764   6000.000000\n",
      "accuracy            0.992732  0.992732  0.992732      0.992732\n",
      "macro avg           0.993868  0.990647  0.992151  75000.000000\n",
      "weighted avg        0.992841  0.992732  0.992687  75000.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ths = open(\"1_08_2020_merged_12.csv\", \"w\")\n",
    "ths.write(\"Dataset,T,CV,ML algorithm,Acc,b_Acc,Precision, Recall , F1-score, kappa ,tra-Time,test-Time,total-Time),Al-Time)\\n\")\n",
    "repetition=100\n",
    "\n",
    "\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.preprocessing import Normalizer\n",
    "print ('%-15s %-3s %-3s %-6s  %-5s %-5s %-5s %-5s %-5s %-5s %-8s %-8s%-8s%-8s'%\n",
    "           (\"Dataset\",\"T\",\"CV\",\"ML alg\",\"Acc\",\"b_Acc\",\"Prec\", \"Rec\" , \"F1\", \"kap\" ,\"tra-T\",\"test-T\",\"total\",\"al-time\"))\n",
    "\n",
    "for ii in ml_list:\n",
    "    class_based_results=pd.DataFrame()#\"\" #pd.DataFrame(0, index=np.arange((len(target_names)+3)), columns=[\"f1-score\",\"precision\",\"recall\",\"support\"])\n",
    "    cm=pd.DataFrame()\n",
    "    cv=0\n",
    "    for i in range(repetition):\n",
    "\n",
    "\n",
    "        #TRAIN\n",
    "        df = pd.read_csv(loop)\n",
    "        df=df.replace({\"Label\": new_labels})\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)#,header=None )\n",
    "        df=df.replace({\"Label\": new_labels})\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",
    "        dname=loop  \n",
    "        results_y=[]\n",
    "        cv+=1\n",
    "        results_y.append(y_test)\n",
    "\n",
    "\n",
    "        precision=[]\n",
    "        recall=[]\n",
    "        f1=[]\n",
    "        accuracy=[]\n",
    "        train_time=[]\n",
    "        test_time=[]\n",
    "        total_time=[]\n",
    "        kappa=[]\n",
    "        accuracy_b=[]\n",
    "\n",
    "            #machine learning algorithm is applied in this section\n",
    "        clf = ml_list[ii]#choose algorithm from ml_list dictionary\n",
    "        second=time.time()\n",
    "        clf.fit(X_train, y_train)\n",
    "        train_time.append(float((time.time()-second)) )\n",
    "        second=time.time()\n",
    "        predict =clf.predict(X_test)\n",
    "        test_time.append(float((time.time()-second)) )\n",
    "        #normal\n",
    "        #lines,class_based_results=score(train_time,test_time,predict,y_test,class_based_results)\n",
    "        #merged\n",
    "        predict,altime=merged(m_test,y_test,predict)\n",
    "        lines,class_based_results=score(altime,train_time,test_time,predict,y_test,class_based_results)\n",
    "\n",
    "        ths.write (lines)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        df_cm = pd.DataFrame(confusion_matrix(y_test, predict))\n",
    "        results_y.append(predict)\n",
    "        if cm.empty:\n",
    "            cm =df_cm\n",
    "        else:\n",
    "            cm = cm.add(df_cm, fill_value=0)\n",
    "\n",
    "\n",
    "print(class_based_results/(repetition)) \n",
    "graph_name=\"merged_confusion matrix\" +str(ii)+\".pdf\"     \n",
    "plt.figure(figsize = (20,14))\n",
    "sns.heatmap(cm,xticklabels=target_names, yticklabels=target_names, annot=True)\n",
    "plt.savefig(graph_name,bbox_inches='tight')#, dpi=400)\n",
    "plt.show()\n",
    "#print(cm)\n",
    "print(\"\\n\\n\\n\") \n",
    "ths.close()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GROUP SIZE 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merged(mac,y_test,predict):\n",
    "    step=8\n",
    "    second=time.time()\n",
    "    mac_test=[]\n",
    "    for i in y_test.index:\n",
    "        mac_test.append(mac[i])\n",
    "\n",
    "    d_list=sorted(list(mac.unique()))\n",
    "    devices={}\n",
    "    for i in d_list:\n",
    "        devices[i]=[]    \n",
    "\n",
    "\n",
    "    new_y=[0]*len(y_test)\n",
    "\n",
    "    for i,ii in enumerate (mac_test):\n",
    "        devices[ii].append(i)\n",
    "        #devices[]\n",
    "    for i in devices:\n",
    "        \n",
    "        a = [devices[i][j:j + step] for j in range(0, len(devices[i]), step)]  \n",
    "        for ii in a:\n",
    "            step_list=[]\n",
    "            for iii in ii:\n",
    "                step_list.append(predict[iii])\n",
    "            add=most_frequent(list(step_list))\n",
    "            for iii in ii:\n",
    "                new_y[iii]=add\n",
    "                \n",
    "    return new_y,time.time()-second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "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__Dataset  0   1   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.06     1.08    3.14    0.93    \n",
      "Aalto__Dataset  1   2   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.21     1.1     3.31    0.97    \n",
      "Aalto__Dataset  2   3   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.25     1.1     3.35    1.12    \n",
      "Aalto__Dataset  3   4   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.12     1.09    3.21    0.98    \n",
      "Aalto__Dataset  4   5   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.08     1.11    3.19    1.1     \n",
      "Aalto__Dataset  5   6   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.1      1.1     3.2     1.04    \n",
      "Aalto__Dataset  6   7   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.13     1.12    3.25    0.95    \n",
      "Aalto__Dataset  7   8   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.07     1.09    3.16    0.97    \n",
      "Aalto__Dataset  8   9   Random  0.99  0.99  0.99  0.99  0.99  0.99  2.15     1.12    3.28    0.86    \n",
      "Aalto__Dataset  9   10  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.1      1.09    3.19    0.99    \n",
      "Aalto__Dataset  10  11  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.14     1.09    3.23    1.12    \n",
      "Aalto__Dataset  11  12  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.14     1.12    3.26    1.01    \n",
      "Aalto__Dataset  12  13  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.15     1.09    3.24    1.01    \n",
      "Aalto__Dataset  13  14  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.11     1.12    3.23    1.13    \n",
      "Aalto__Dataset  14  15  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.32     1.21    3.53    1.14    \n",
      "Aalto__Dataset  15  16  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.38     1.15    3.53    1.14    \n",
      "Aalto__Dataset  16  17  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.25     1.12    3.37    1.09    \n",
      "Aalto__Dataset  17  18  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.36     1.16    3.52    0.99    \n",
      "Aalto__Dataset  18  19  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.29     1.17    3.46    1.07    \n",
      "Aalto__Dataset  19  20  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.15     1.18    3.32    1.09    \n",
      "Aalto__Dataset  20  21  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.21     1.19    3.4     1.12    \n",
      "Aalto__Dataset  21  22  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.09     1.12    3.21    1.04    \n",
      "Aalto__Dataset  22  23  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.23     1.17    3.4     1.1     \n",
      "Aalto__Dataset  23  24  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.12     1.13    3.25    1.01    \n",
      "Aalto__Dataset  24  25  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.16     1.08    3.24    1.0     \n",
      "Aalto__Dataset  25  26  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.19     1.08    3.27    1.07    \n",
      "Aalto__Dataset  26  27  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.27     1.13    3.39    1.11    \n",
      "Aalto__Dataset  27  28  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.18     1.11    3.29    0.99    \n",
      "Aalto__Dataset  28  29  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.31     1.15    3.46    0.99    \n",
      "Aalto__Dataset  29  30  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.23     1.18    3.41    1.1     \n",
      "Aalto__Dataset  30  31  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.35     1.13    3.48    1.06    \n",
      "Aalto__Dataset  31  32  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.21     1.13    3.34    0.99    \n",
      "Aalto__Dataset  32  33  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.11     1.09    3.2     1.09    \n",
      "Aalto__Dataset  33  34  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.1      1.09    3.2     1.03    \n",
      "Aalto__Dataset  34  35  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.07     1.1     3.17    1.03    \n",
      "Aalto__Dataset  35  36  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.38     1.2     3.59    1.05    \n",
      "Aalto__Dataset  36  37  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.17     1.33    3.5     1.3     \n",
      "Aalto__Dataset  37  38  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.52     1.55    4.08    1.2     \n",
      "Aalto__Dataset  38  39  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.25     1.14    3.39    0.95    \n",
      "Aalto__Dataset  39  40  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.21     1.24    3.45    0.97    \n",
      "Aalto__Dataset  40  41  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.15     1.15    3.3     1.03    \n",
      "Aalto__Dataset  41  42  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.15     1.13    3.28    1.02    \n",
      "Aalto__Dataset  42  43  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.18     1.15    3.33    1.02    \n",
      "Aalto__Dataset  43  44  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.12     1.1     3.22    1.02    \n",
      "Aalto__Dataset  44  45  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.4      1.1     3.5     0.99    \n",
      "Aalto__Dataset  45  46  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.09     1.11    3.2     1.01    \n",
      "Aalto__Dataset  46  47  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.14     1.12    3.26    1.07    \n",
      "Aalto__Dataset  47  48  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.12     1.11    3.23    1.01    \n",
      "Aalto__Dataset  48  49  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.06     1.09    3.15    0.96    \n",
      "Aalto__Dataset  49  50  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.08     1.09    3.16    0.96    \n",
      "Aalto__Dataset  50  51  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.09     1.1     3.19    1.26    \n",
      "Aalto__Dataset  51  52  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.24     1.15    3.4     0.99    \n",
      "Aalto__Dataset  52  53  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.12     1.12    3.25    0.96    \n",
      "Aalto__Dataset  53  54  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.15     1.12    3.27    1.0     \n",
      "Aalto__Dataset  54  55  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.32     1.32    3.64    1.02    \n",
      "Aalto__Dataset  55  56  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.09     1.09    3.19    1.16    \n",
      "Aalto__Dataset  56  57  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.06     1.09    3.15    1.1     \n",
      "Aalto__Dataset  57  58  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.12     1.12    3.24    0.98    \n",
      "Aalto__Dataset  58  59  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.1      1.09    3.19    1.09    \n",
      "Aalto__Dataset  59  60  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.43     1.17    3.6     1.11    \n",
      "Aalto__Dataset  60  61  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.35     1.11    3.46    1.06    \n",
      "Aalto__Dataset  61  62  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.13     1.17    3.3     1.07    \n",
      "Aalto__Dataset  62  63  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.18     1.21    3.38    1.0     \n",
      "Aalto__Dataset  63  64  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.16     1.22    3.38    1.12    \n",
      "Aalto__Dataset  64  65  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.14     1.08    3.22    0.85    \n",
      "Aalto__Dataset  65  66  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.05     1.08    3.13    0.98    \n",
      "Aalto__Dataset  66  67  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.11     1.15    3.26    0.99    \n",
      "Aalto__Dataset  67  68  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.09     1.11    3.19    0.97    \n",
      "Aalto__Dataset  68  69  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.15     1.13    3.28    1.05    \n",
      "Aalto__Dataset  69  70  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.11     1.12    3.23    1.04    \n",
      "Aalto__Dataset  70  71  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.12     1.11    3.23    0.94    \n",
      "Aalto__Dataset  71  72  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.24     1.15    3.39    0.98    \n",
      "Aalto__Dataset  72  73  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.25     1.14    3.39    1.03    \n",
      "Aalto__Dataset  73  74  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.13     1.14    3.28    1.02    \n",
      "Aalto__Dataset  74  75  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.18     1.13    3.31    1.0     \n",
      "Aalto__Dataset  75  76  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.3      1.11    3.41    1.04    \n",
      "Aalto__Dataset  76  77  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.49     2.29    4.78    1.26    \n",
      "Aalto__Dataset  77  78  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.39     1.17    3.56    1.76    \n",
      "Aalto__Dataset  78  79  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.21     1.35    3.56    1.21    \n",
      "Aalto__Dataset  79  80  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.28     1.12    3.4     1.02    \n",
      "Aalto__Dataset  80  81  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.11     1.17    3.28    1.19    \n",
      "Aalto__Dataset  81  82  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.11     1.12    3.23    0.94    \n",
      "Aalto__Dataset  82  83  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.2      1.09    3.29    1.17    \n",
      "Aalto__Dataset  83  84  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.23     1.11    3.34    0.9     \n",
      "Aalto__Dataset  84  85  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.13     1.1     3.23    1.08    \n",
      "Aalto__Dataset  85  86  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.16     1.12    3.29    1.13    \n",
      "Aalto__Dataset  86  87  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.08     1.13    3.21    1.07    \n",
      "Aalto__Dataset  87  88  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.1      1.23    3.33    1.23    \n",
      "Aalto__Dataset  88  89  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.46     1.2     3.66    1.0     \n",
      "Aalto__Dataset  89  90  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.1      1.12    3.22    1.05    \n",
      "Aalto__Dataset  90  91  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.16     1.13    3.29    1.11    \n",
      "Aalto__Dataset  91  92  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.23     1.15    3.37    0.97    \n",
      "Aalto__Dataset  92  93  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.27     1.15    3.42    1.01    \n",
      "Aalto__Dataset  93  94  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.15     1.11    3.25    0.91    \n",
      "Aalto__Dataset  94  95  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.14     1.1     3.24    0.89    \n",
      "Aalto__Dataset  95  96  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.1      1.13    3.23    0.98    \n",
      "Aalto__Dataset  96  97  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.08     1.09    3.17    0.84    \n",
      "Aalto__Dataset  97  98  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.16     1.12    3.28    0.93    \n",
      "Aalto__Dataset  98  99  Random  0.99  0.99  0.99  0.99  0.99  0.99  2.11     1.15    3.26    0.99    \n",
      "Aalto__Dataset  99  100 Random  0.99  0.99  0.99  0.99  0.99  0.99  2.09     1.09    3.18    0.91    \n",
      "                   precision    recall  f1-score       support\n",
      "Aria                0.980195  0.973333  0.976752   3000.000000\n",
      "D-LinkCam           0.997586  1.000000  0.998792   3000.000000\n",
      "D-LinkDayCam        0.997660  0.994667  0.996161   3000.000000\n",
      "D-LinkDevice        0.999125  0.993547  0.996327   3000.000000\n",
      "D-Sensors           0.996878  1.000000  0.998437  12000.000000\n",
      "EdimaxCam           1.000000  0.993840  0.996910   3000.000000\n",
      "EdimaxPlug          0.992102  0.990667  0.991384   6000.000000\n",
      "EdnetCam            0.990401  0.997333  0.993855   3000.000000\n",
      "EdnetGateway        0.999846  0.960987  0.980028   3000.000000\n",
      "HomeMaticPlug       1.000000  1.000000  1.000000   3000.000000\n",
      "Hue-Device          0.999600  1.000000  0.999800   3000.000000\n",
      "Lightify            1.000000  1.000000  1.000000   3000.000000\n",
      "MAXGateway          0.999663  0.989333  0.994471   3000.000000\n",
      "TP-LinkPlugHS       0.992743  0.993333  0.993038   6000.000000\n",
      "WeMoInsightSwitch   0.986390  0.947927  0.966775   3000.000000\n",
      "WeMoLink            0.988019  1.000000  0.993973   3000.000000\n",
      "WeMoSwitch          0.988871  0.927880  0.957405   3000.000000\n",
      "Withings            1.000000  0.976000  0.987854   3000.000000\n",
      "smart_Kettle        0.924712  1.000000  0.960883   6000.000000\n",
      "accuracy            0.988914  0.988914  0.988914      0.988914\n",
      "macro avg           0.991252  0.986255  0.988571  75000.000000\n",
      "weighted avg        0.989359  0.988914  0.988938  75000.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ths = open(\"1_08_2020_merged_9.csv\", \"w\")\n",
    "ths.write(\"Dataset,T,CV,ML algorithm,Acc,b_Acc,Precision, Recall , F1-score, kappa ,tra-Time,test-Time,total-Time),Al-Time)\\n\")\n",
    "repetition=100\n",
    "\n",
    "\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.preprocessing import Normalizer\n",
    "print ('%-15s %-3s %-3s %-6s  %-5s %-5s %-5s %-5s %-5s %-5s %-8s %-8s%-8s%-8s'%\n",
    "           (\"Dataset\",\"T\",\"CV\",\"ML alg\",\"Acc\",\"b_Acc\",\"Prec\", \"Rec\" , \"F1\", \"kap\" ,\"tra-T\",\"test-T\",\"total\",\"al-time\"))\n",
    "\n",
    "for ii in ml_list:\n",
    "    class_based_results=pd.DataFrame()#\"\" #pd.DataFrame(0, index=np.arange((len(target_names)+3)), columns=[\"f1-score\",\"precision\",\"recall\",\"support\"])\n",
    "    cm=pd.DataFrame()\n",
    "    cv=0\n",
    "    for i in range(repetition):\n",
    "\n",
    "\n",
    "        #TRAIN\n",
    "        df = pd.read_csv(loop)\n",
    "        df=df.replace({\"Label\": new_labels})\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)#,header=None )\n",
    "        df=df.replace({\"Label\": new_labels})\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",
    "        dname=loop  \n",
    "        results_y=[]\n",
    "        cv+=1\n",
    "        results_y.append(y_test)\n",
    "\n",
    "\n",
    "        precision=[]\n",
    "        recall=[]\n",
    "        f1=[]\n",
    "        accuracy=[]\n",
    "        train_time=[]\n",
    "        test_time=[]\n",
    "        total_time=[]\n",
    "        kappa=[]\n",
    "        accuracy_b=[]\n",
    "\n",
    "            #machine learning algorithm is applied in this section\n",
    "        clf = ml_list[ii]#choose algorithm from ml_list dictionary\n",
    "        second=time.time()\n",
    "        clf.fit(X_train, y_train)\n",
    "        train_time.append(float((time.time()-second)) )\n",
    "        second=time.time()\n",
    "        predict =clf.predict(X_test)\n",
    "        test_time.append(float((time.time()-second)) )\n",
    "        #normal\n",
    "        #lines,class_based_results=score(train_time,test_time,predict,y_test,class_based_results)\n",
    "        #merged\n",
    "        predict,altime=merged(m_test,y_test,predict)\n",
    "        lines,class_based_results=score(altime,train_time,test_time,predict,y_test,class_based_results)\n",
    "\n",
    "        ths.write (lines)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        df_cm = pd.DataFrame(confusion_matrix(y_test, predict))\n",
    "        results_y.append(predict)\n",
    "        if cm.empty:\n",
    "            cm =df_cm\n",
    "        else:\n",
    "            cm = cm.add(df_cm, fill_value=0)\n",
    "\n",
    "\n",
    "print(class_based_results/(repetition)) \n",
    "graph_name=\"merged_confusion matrix\" +str(ii)+\".pdf\"     \n",
    "plt.figure(figsize = (20,14))\n",
    "sns.heatmap(cm,xticklabels=target_names, yticklabels=target_names, annot=True)\n",
    "plt.savefig(graph_name,bbox_inches='tight')#, dpi=400)\n",
    "plt.show()\n",
    "#print(cm)\n",
    "print(\"\\n\\n\\n\") \n",
    "ths.close()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GROUP SIZE 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merged(mac,y_test,predict):\n",
    "    step=6\n",
    "    second=time.time()\n",
    "    mac_test=[]\n",
    "    for i in y_test.index:\n",
    "        mac_test.append(mac[i])\n",
    "\n",
    "    d_list=sorted(list(mac.unique()))\n",
    "    devices={}\n",
    "    for i in d_list:\n",
    "        devices[i]=[]    \n",
    "\n",
    "\n",
    "    new_y=[0]*len(y_test)\n",
    "\n",
    "    for i,ii in enumerate (mac_test):\n",
    "        devices[ii].append(i)\n",
    "        #devices[]\n",
    "    for i in devices:\n",
    "        \n",
    "        a = [devices[i][j:j + step] for j in range(0, len(devices[i]), step)]  \n",
    "        for ii in a:\n",
    "            step_list=[]\n",
    "            for iii in ii:\n",
    "                step_list.append(predict[iii])\n",
    "            add=most_frequent(list(step_list))\n",
    "            for iii in ii:\n",
    "                new_y[iii]=add\n",
    "                \n",
    "    return new_y,time.time()-second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "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__Dataset  0   1   Random  0.98  0.98  0.99  0.98  0.98  0.98  2.17     1.11    3.27    0.99    \n",
      "Aalto__Dataset  1   2   Random  0.98  0.98  0.99  0.98  0.98  0.98  2.21     1.11    3.32    1.03    \n",
      "Aalto__Dataset  2   3   Random  0.98  0.98  0.99  0.98  0.98  0.98  2.19     1.23    3.42    1.14    \n",
      "Aalto__Dataset  3   4   Random  0.98  0.98  0.99  0.98  0.98  0.98  2.08     1.09    3.17    1.1     \n",
      "Aalto__Dataset  4   5   Random  0.98  0.98  0.99  0.98  0.98  0.98  2.12     1.2     3.32    1.01    \n",
      "Aalto__Dataset  5   6   Random  0.98  0.98  0.99  0.98  0.98  0.98  2.2      1.17    3.37    1.08    \n",
      "Aalto__Dataset  6   7   Random  0.98  0.98  0.99  0.98  0.98  0.98  2.75     1.17    3.91    1.15    \n",
      "Aalto__Dataset  7   8   Random  0.98  0.98  0.99  0.98  0.98  0.98  2.22     1.14    3.36    1.08    \n",
      "Aalto__Dataset  8   9   Random  0.98  0.98  0.99  0.98  0.98  0.98  2.08     1.09    3.17    0.94    \n",
      "Aalto__Dataset  9   10  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.26     1.13    3.39    1.14    \n",
      "Aalto__Dataset  10  11  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.32     1.15    3.47    1.03    \n",
      "Aalto__Dataset  11  12  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.27     1.15    3.42    1.17    \n",
      "Aalto__Dataset  12  13  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.59     1.19    3.77    1.12    \n",
      "Aalto__Dataset  13  14  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.62     1.26    3.88    1.26    \n",
      "Aalto__Dataset  14  15  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.53     1.27    3.8     1.15    \n",
      "Aalto__Dataset  15  16  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.51     1.19    3.7     1.13    \n",
      "Aalto__Dataset  16  17  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.56     1.37    3.93    1.09    \n",
      "Aalto__Dataset  17  18  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.2      1.09    3.29    0.85    \n",
      "Aalto__Dataset  18  19  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.03     1.29    3.32    1.02    \n",
      "Aalto__Dataset  19  20  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.35     1.15    3.51    1.01    \n",
      "Aalto__Dataset  20  21  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.05     1.2     3.25    0.97    \n",
      "Aalto__Dataset  21  22  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.23     1.16    3.4     1.05    \n",
      "Aalto__Dataset  22  23  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.18     1.11    3.29    0.9     \n",
      "Aalto__Dataset  23  24  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.19     1.19    3.38    1.01    \n",
      "Aalto__Dataset  24  25  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.12     1.09    3.21    1.12    \n",
      "Aalto__Dataset  25  26  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.06     1.08    3.14    1.02    \n",
      "Aalto__Dataset  26  27  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.06     1.09    3.15    1.03    \n",
      "Aalto__Dataset  27  28  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.04     1.09    3.13    0.94    \n",
      "Aalto__Dataset  28  29  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.03     1.08    3.12    0.98    \n",
      "Aalto__Dataset  29  30  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.17     1.14    3.3     1.08    \n",
      "Aalto__Dataset  30  31  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.26     1.2     3.46    1.17    \n",
      "Aalto__Dataset  31  32  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.16     1.16    3.32    0.99    \n",
      "Aalto__Dataset  32  33  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.17     1.15    3.32    0.88    \n",
      "Aalto__Dataset  33  34  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.09     1.1     3.18    0.92    \n",
      "Aalto__Dataset  34  35  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.26     1.22    3.49    0.97    \n",
      "Aalto__Dataset  35  36  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.13     1.2     3.33    1.08    \n",
      "Aalto__Dataset  36  37  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.18     1.09    3.28    0.86    \n",
      "Aalto__Dataset  37  38  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.08     1.23    3.32    1.01    \n",
      "Aalto__Dataset  38  39  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.5      1.52    4.02    1.18    \n",
      "Aalto__Dataset  39  40  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.33     1.1     3.44    1.0     \n",
      "Aalto__Dataset  40  41  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.13     1.18    3.31    1.14    \n",
      "Aalto__Dataset  41  42  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.1      1.11    3.21    0.99    \n",
      "Aalto__Dataset  42  43  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.11     1.11    3.22    0.92    \n",
      "Aalto__Dataset  43  44  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.22     1.14    3.36    1.11    \n",
      "Aalto__Dataset  44  45  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.06     1.2     3.26    1.06    \n",
      "Aalto__Dataset  45  46  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.52     1.26    3.78    1.12    \n",
      "Aalto__Dataset  46  47  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.51     1.24    3.75    1.07    \n",
      "Aalto__Dataset  47  48  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.29     1.14    3.44    0.95    \n",
      "Aalto__Dataset  48  49  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.33     1.11    3.44    1.58    \n",
      "Aalto__Dataset  49  50  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.16     1.09    3.24    0.97    \n",
      "Aalto__Dataset  50  51  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.09     1.1     3.18    0.94    \n",
      "Aalto__Dataset  51  52  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.17     1.1     3.27    1.05    \n",
      "Aalto__Dataset  52  53  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.27     1.31    3.58    1.18    \n",
      "Aalto__Dataset  53  54  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.25     1.22    3.48    1.16    \n",
      "Aalto__Dataset  54  55  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.28     1.25    3.54    0.92    \n",
      "Aalto__Dataset  55  56  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.27     1.12    3.39    1.08    \n",
      "Aalto__Dataset  56  57  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.28     1.85    4.13    1.25    \n",
      "Aalto__Dataset  57  58  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.38     1.16    3.54    1.1     \n",
      "Aalto__Dataset  58  59  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.31     1.21    3.52    1.24    \n",
      "Aalto__Dataset  59  60  Random  0.98  0.98  0.99  0.98  0.98  0.98  3.14     1.55    4.69    1.71    \n",
      "Aalto__Dataset  60  61  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.17     1.16    3.32    1.08    \n",
      "Aalto__Dataset  61  62  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.4      1.12    3.52    0.98    \n",
      "Aalto__Dataset  62  63  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.12     1.18    3.3     1.16    \n",
      "Aalto__Dataset  63  64  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.22     1.21    3.43    1.04    \n",
      "Aalto__Dataset  64  65  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.23     1.23    3.46    1.16    \n",
      "Aalto__Dataset  65  66  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.24     1.12    3.36    0.89    \n",
      "Aalto__Dataset  66  67  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.12     1.12    3.24    1.04    \n",
      "Aalto__Dataset  67  68  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.15     1.16    3.31    1.06    \n",
      "Aalto__Dataset  68  69  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.19     1.1     3.29    0.87    \n",
      "Aalto__Dataset  69  70  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.28     1.23    3.5     0.99    \n",
      "Aalto__Dataset  70  71  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.14     1.11    3.25    0.98    \n",
      "Aalto__Dataset  71  72  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.16     1.09    3.25    1.2     \n",
      "Aalto__Dataset  72  73  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.28     1.15    3.43    1.04    \n",
      "Aalto__Dataset  73  74  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.09     1.15    3.24    0.95    \n",
      "Aalto__Dataset  74  75  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.35     1.15    3.5     1.02    \n",
      "Aalto__Dataset  75  76  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.13     1.12    3.25    1.05    \n",
      "Aalto__Dataset  76  77  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.17     1.23    3.4     1.13    \n",
      "Aalto__Dataset  77  78  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.18     1.12    3.29    1.21    \n",
      "Aalto__Dataset  78  79  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.27     1.23    3.5     0.86    \n",
      "Aalto__Dataset  79  80  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.3      1.14    3.44    0.96    \n",
      "Aalto__Dataset  80  81  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.44     1.16    3.59    0.94    \n",
      "Aalto__Dataset  81  82  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.2      1.21    3.41    1.13    \n",
      "Aalto__Dataset  82  83  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.36     1.14    3.5     1.22    \n",
      "Aalto__Dataset  83  84  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.13     1.3     3.43    1.11    \n",
      "Aalto__Dataset  84  85  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.16     1.3     3.46    1.05    \n",
      "Aalto__Dataset  85  86  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.16     1.16    3.32    1.02    \n",
      "Aalto__Dataset  86  87  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.19     1.13    3.32    1.06    \n",
      "Aalto__Dataset  87  88  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.22     1.14    3.36    1.03    \n",
      "Aalto__Dataset  88  89  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.16     1.13    3.3     1.05    \n",
      "Aalto__Dataset  89  90  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.14     1.17    3.31    1.03    \n",
      "Aalto__Dataset  90  91  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.4      1.22    3.61    0.96    \n",
      "Aalto__Dataset  91  92  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.41     1.18    3.58    0.97    \n",
      "Aalto__Dataset  92  93  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.37     1.59    3.95    1.12    \n",
      "Aalto__Dataset  93  94  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.43     1.24    3.67    1.13    \n",
      "Aalto__Dataset  94  95  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.22     1.18    3.4     1.08    \n",
      "Aalto__Dataset  95  96  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.24     1.12    3.35    1.17    \n",
      "Aalto__Dataset  96  97  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.18     1.11    3.29    1.16    \n",
      "Aalto__Dataset  97  98  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.3      1.18    3.48    1.16    \n",
      "Aalto__Dataset  98  99  Random  0.98  0.98  0.99  0.98  0.98  0.98  2.39     1.21    3.6     1.2     \n",
      "Aalto__Dataset  99  100 Random  0.98  0.98  0.99  0.98  0.98  0.98  2.35     1.15    3.49    1.39    \n",
      "                   precision    recall  f1-score       support\n",
      "Aria                0.972644  0.960000  0.966281   3000.000000\n",
      "D-LinkCam           0.997550  1.000000  0.998773   3000.000000\n",
      "D-LinkDayCam        0.993479  0.974000  0.983643   3000.000000\n",
      "D-LinkDevice        0.999071  0.985660  0.992318   3000.000000\n",
      "D-Sensors           0.995375  0.999500  0.997433  12000.000000\n",
      "EdimaxCam           1.000000  0.981213  0.990517   3000.000000\n",
      "EdimaxPlug          0.992004  0.976840  0.984364   6000.000000\n",
      "EdnetCam            0.990349  0.992000  0.991174   3000.000000\n",
      "EdnetGateway        0.999543  0.952960  0.975692   3000.000000\n",
      "HomeMaticPlug       1.000000  1.000000  1.000000   3000.000000\n",
      "Hue-Device          0.999587  1.000000  0.999793   3000.000000\n",
      "Lightify            1.000000  0.988000  0.993964   3000.000000\n",
      "MAXGateway          0.999654  0.962000  0.980465   3000.000000\n",
      "TP-LinkPlugHS       0.992721  0.986880  0.989792   6000.000000\n",
      "WeMoInsightSwitch   0.986571  0.947143  0.966454   3000.000000\n",
      "WeMoLink            0.987453  0.999900  0.993637   3000.000000\n",
      "WeMoSwitch          0.988704  0.927973  0.957376   3000.000000\n",
      "Withings            1.000000  0.960000  0.979592   3000.000000\n",
      "smart_Kettle        0.865395  1.000000  0.927841   6000.000000\n",
      "accuracy            0.982252  0.982252  0.982252      0.982252\n",
      "macro avg           0.987374  0.978635  0.982585  75000.000000\n",
      "weighted avg        0.983854  0.982252  0.982536  75000.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ths = open(\"1_08_2020_merged_6.csv\", \"w\")\n",
    "ths.write(\"Dataset,T,CV,ML algorithm,Acc,b_Acc,Precision, Recall , F1-score, kappa ,tra-Time,test-Time,total-Time),Al-Time)\\n\")\n",
    "repetition=100\n",
    "\n",
    "\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.preprocessing import Normalizer\n",
    "print ('%-15s %-3s %-3s %-6s  %-5s %-5s %-5s %-5s %-5s %-5s %-8s %-8s%-8s%-8s'%\n",
    "           (\"Dataset\",\"T\",\"CV\",\"ML alg\",\"Acc\",\"b_Acc\",\"Prec\", \"Rec\" , \"F1\", \"kap\" ,\"tra-T\",\"test-T\",\"total\",\"al-time\"))\n",
    "\n",
    "for ii in ml_list:\n",
    "    class_based_results=pd.DataFrame()#\"\" #pd.DataFrame(0, index=np.arange((len(target_names)+3)), columns=[\"f1-score\",\"precision\",\"recall\",\"support\"])\n",
    "    cm=pd.DataFrame()\n",
    "    cv=0\n",
    "    for i in range(repetition):\n",
    "\n",
    "\n",
    "        #TRAIN\n",
    "        df = pd.read_csv(loop)\n",
    "        df=df.replace({\"Label\": new_labels})\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)#,header=None )\n",
    "        df=df.replace({\"Label\": new_labels})\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",
    "        dname=loop  \n",
    "        results_y=[]\n",
    "        cv+=1\n",
    "        results_y.append(y_test)\n",
    "\n",
    "\n",
    "        precision=[]\n",
    "        recall=[]\n",
    "        f1=[]\n",
    "        accuracy=[]\n",
    "        train_time=[]\n",
    "        test_time=[]\n",
    "        total_time=[]\n",
    "        kappa=[]\n",
    "        accuracy_b=[]\n",
    "\n",
    "            #machine learning algorithm is applied in this section\n",
    "        clf = ml_list[ii]#choose algorithm from ml_list dictionary\n",
    "        second=time.time()\n",
    "        clf.fit(X_train, y_train)\n",
    "        train_time.append(float((time.time()-second)) )\n",
    "        second=time.time()\n",
    "        predict =clf.predict(X_test)\n",
    "        test_time.append(float((time.time()-second)) )\n",
    "        #normal\n",
    "        #lines,class_based_results=score(train_time,test_time,predict,y_test,class_based_results)\n",
    "        #merged\n",
    "        predict,altime=merged(m_test,y_test,predict)\n",
    "        lines,class_based_results=score(altime,train_time,test_time,predict,y_test,class_based_results)\n",
    "\n",
    "        ths.write (lines)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        df_cm = pd.DataFrame(confusion_matrix(y_test, predict))\n",
    "        results_y.append(predict)\n",
    "        if cm.empty:\n",
    "            cm =df_cm\n",
    "        else:\n",
    "            cm = cm.add(df_cm, fill_value=0)\n",
    "\n",
    "\n",
    "print(class_based_results/(repetition)) \n",
    "graph_name=\"merged_confusion matrix\" +str(ii)+\".pdf\"     \n",
    "plt.figure(figsize = (20,14))\n",
    "sns.heatmap(cm,xticklabels=target_names, yticklabels=target_names, annot=True)\n",
    "plt.savefig(graph_name,bbox_inches='tight')#, dpi=400)\n",
    "plt.show()\n",
    "#print(cm)\n",
    "print(\"\\n\\n\\n\") \n",
    "ths.close()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GROUP SIZE 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merged(mac,y_test,predict):\n",
    "    step=3\n",
    "    second=time.time()\n",
    "    mac_test=[]\n",
    "    for i in y_test.index:\n",
    "        mac_test.append(mac[i])\n",
    "\n",
    "    d_list=sorted(list(mac.unique()))\n",
    "    devices={}\n",
    "    for i in d_list:\n",
    "        devices[i]=[]    \n",
    "\n",
    "\n",
    "    new_y=[0]*len(y_test)\n",
    "\n",
    "    for i,ii in enumerate (mac_test):\n",
    "        devices[ii].append(i)\n",
    "        #devices[]\n",
    "    for i in devices:\n",
    "        \n",
    "        a = [devices[i][j:j + step] for j in range(0, len(devices[i]), step)]  \n",
    "        for ii in a:\n",
    "            step_list=[]\n",
    "            for iii in ii:\n",
    "                step_list.append(predict[iii])\n",
    "            add=most_frequent(list(step_list))\n",
    "            for iii in ii:\n",
    "                new_y[iii]=add\n",
    "                \n",
    "    return new_y,time.time()-second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "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__Dataset  0   1   Random  0.95  0.94  0.97  0.94  0.95  0.95  2.38     1.19    3.57    1.12    \n",
      "Aalto__Dataset  1   2   Random  0.95  0.94  0.97  0.94  0.95  0.95  2.35     1.23    3.59    1.18    \n",
      "Aalto__Dataset  2   3   Random  0.95  0.94  0.97  0.94  0.95  0.95  2.54     1.22    3.77    1.24    \n",
      "Aalto__Dataset  3   4   Random  0.95  0.94  0.97  0.94  0.95  0.95  2.63     1.28    3.9     1.09    \n",
      "Aalto__Dataset  4   5   Random  0.95  0.94  0.97  0.94  0.95  0.95  2.5      1.26    3.76    1.13    \n",
      "Aalto__Dataset  5   6   Random  0.95  0.94  0.97  0.94  0.95  0.95  2.53     1.21    3.74    1.29    \n",
      "Aalto__Dataset  6   7   Random  0.95  0.94  0.97  0.94  0.95  0.95  2.38     1.12    3.5     1.03    \n",
      "Aalto__Dataset  7   8   Random  0.95  0.94  0.97  0.94  0.95  0.95  2.17     1.1     3.27    1.04    \n",
      "Aalto__Dataset  8   9   Random  0.95  0.94  0.97  0.94  0.95  0.95  2.15     1.09    3.24    1.16    \n",
      "Aalto__Dataset  9   10  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.13     1.15    3.28    1.07    \n",
      "Aalto__Dataset  10  11  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.34     1.14    3.47    1.02    \n",
      "Aalto__Dataset  11  12  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.23     1.14    3.37    1.1     \n",
      "Aalto__Dataset  12  13  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.32     1.18    3.5     1.14    \n",
      "Aalto__Dataset  13  14  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.18     1.13    3.31    1.15    \n",
      "Aalto__Dataset  14  15  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.18     1.16    3.34    1.05    \n",
      "Aalto__Dataset  15  16  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.3      1.15    3.45    1.06    \n",
      "Aalto__Dataset  16  17  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.41     1.15    3.55    1.04    \n",
      "Aalto__Dataset  17  18  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.21     1.16    3.37    0.97    \n",
      "Aalto__Dataset  18  19  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.28     1.18    3.46    0.92    \n",
      "Aalto__Dataset  19  20  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.26     1.13    3.4     1.04    \n",
      "Aalto__Dataset  20  21  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.3      1.21    3.51    1.04    \n",
      "Aalto__Dataset  21  22  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.21     1.19    3.4     0.93    \n",
      "Aalto__Dataset  22  23  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.23     1.14    3.37    0.87    \n",
      "Aalto__Dataset  23  24  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.06     1.16    3.23    0.96    \n",
      "Aalto__Dataset  24  25  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.12     1.18    3.3     1.05    \n",
      "Aalto__Dataset  25  26  Random  0.95  0.95  0.97  0.95  0.96  0.95  2.33     1.13    3.46    1.16    \n",
      "Aalto__Dataset  26  27  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.32     1.2     3.52    1.12    \n",
      "Aalto__Dataset  27  28  Random  0.95  0.94  0.97  0.94  0.96  0.95  2.19     1.08    3.28    0.93    \n",
      "Aalto__Dataset  28  29  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.07     1.08    3.15    0.87    \n",
      "Aalto__Dataset  29  30  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.18     1.17    3.36    1.13    \n",
      "Aalto__Dataset  30  31  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.42     1.29    3.71    1.19    \n",
      "Aalto__Dataset  31  32  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.23     1.08    3.31    1.07    \n",
      "Aalto__Dataset  32  33  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.06     1.08    3.14    1.01    \n",
      "Aalto__Dataset  33  34  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.18     1.2     3.38    1.17    \n",
      "Aalto__Dataset  34  35  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.29     1.18    3.47    1.18    \n",
      "Aalto__Dataset  35  36  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.22     1.09    3.31    1.1     \n",
      "Aalto__Dataset  36  37  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.13     1.23    3.35    1.19    \n",
      "Aalto__Dataset  37  38  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.22     1.23    3.46    1.14    \n",
      "Aalto__Dataset  38  39  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.56     1.25    3.81    1.17    \n",
      "Aalto__Dataset  39  40  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.39     1.28    3.67    1.07    \n",
      "Aalto__Dataset  40  41  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.13     1.17    3.3     1.47    \n",
      "Aalto__Dataset  41  42  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.6      1.29    3.88    1.2     \n",
      "Aalto__Dataset  42  43  Random  0.95  0.94  0.97  0.94  0.96  0.95  2.27     1.16    3.43    1.05    \n",
      "Aalto__Dataset  43  44  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.27     1.15    3.42    0.98    \n",
      "Aalto__Dataset  44  45  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.16     1.14    3.3     1.06    \n",
      "Aalto__Dataset  45  46  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.19     1.15    3.33    1.11    \n",
      "Aalto__Dataset  46  47  Random  0.95  0.94  0.97  0.94  0.96  0.95  2.23     1.16    3.39    1.03    \n",
      "Aalto__Dataset  47  48  Random  0.95  0.94  0.97  0.94  0.96  0.95  2.21     1.16    3.37    1.04    \n",
      "Aalto__Dataset  48  49  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.46     1.25    3.71    1.18    \n",
      "Aalto__Dataset  49  50  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.38     1.32    3.7     1.08    \n",
      "Aalto__Dataset  50  51  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.51     1.31    3.82    1.06    \n",
      "Aalto__Dataset  51  52  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.51     1.21    3.72    1.32    \n",
      "Aalto__Dataset  52  53  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.68     1.31    3.99    1.13    \n",
      "Aalto__Dataset  53  54  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.39     1.41    3.79    1.06    \n",
      "Aalto__Dataset  54  55  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.41     1.16    3.57    1.0     \n",
      "Aalto__Dataset  55  56  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.22     1.17    3.39    1.05    \n",
      "Aalto__Dataset  56  57  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.21     1.33    3.54    1.04    \n",
      "Aalto__Dataset  57  58  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.17     1.29    3.46    1.03    \n",
      "Aalto__Dataset  58  59  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.33     1.37    3.7     1.36    \n",
      "Aalto__Dataset  59  60  Random  0.95  0.94  0.97  0.94  0.96  0.95  2.23     1.21    3.43    1.15    \n",
      "Aalto__Dataset  60  61  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.54     1.28    3.82    1.04    \n",
      "Aalto__Dataset  61  62  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.18     1.19    3.37    1.05    \n",
      "Aalto__Dataset  62  63  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.19     1.17    3.36    1.04    \n",
      "Aalto__Dataset  63  64  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.37     1.19    3.55    1.07    \n",
      "Aalto__Dataset  64  65  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.37     1.2     3.57    1.04    \n",
      "Aalto__Dataset  65  66  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.22     1.32    3.55    1.08    \n",
      "Aalto__Dataset  66  67  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.33     1.18    3.51    1.1     \n",
      "Aalto__Dataset  67  68  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.21     1.17    3.38    1.05    \n",
      "Aalto__Dataset  68  69  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.24     1.17    3.41    1.12    \n",
      "Aalto__Dataset  69  70  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.17     1.17    3.34    1.41    \n",
      "Aalto__Dataset  70  71  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.62     1.29    3.91    1.09    \n",
      "Aalto__Dataset  71  72  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.61     1.24    3.85    1.07    \n",
      "Aalto__Dataset  72  73  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.36     1.22    3.57    1.16    \n",
      "Aalto__Dataset  73  74  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.39     1.25    3.64    1.12    \n",
      "Aalto__Dataset  74  75  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.39     1.17    3.56    1.03    \n",
      "Aalto__Dataset  75  76  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.2      1.17    3.37    1.35    \n",
      "Aalto__Dataset  76  77  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.48     1.26    3.75    1.11    \n",
      "Aalto__Dataset  77  78  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.51     1.35    3.85    1.13    \n",
      "Aalto__Dataset  78  79  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.33     1.16    3.49    0.97    \n",
      "Aalto__Dataset  79  80  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.37     1.28    3.66    1.18    \n",
      "Aalto__Dataset  80  81  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.54     1.23    3.77    1.2     \n",
      "Aalto__Dataset  81  82  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.33     1.24    3.56    1.11    \n",
      "Aalto__Dataset  82  83  Random  0.95  0.95  0.97  0.95  0.96  0.95  2.29     1.24    3.53    1.06    \n",
      "Aalto__Dataset  83  84  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.29     1.23    3.52    1.26    \n",
      "Aalto__Dataset  84  85  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.37     1.27    3.64    1.12    \n",
      "Aalto__Dataset  85  86  Random  0.95  0.95  0.97  0.95  0.96  0.95  2.3      1.33    3.62    1.36    \n",
      "Aalto__Dataset  86  87  Random  0.95  0.95  0.97  0.95  0.96  0.95  2.61     1.32    3.92    1.24    \n",
      "Aalto__Dataset  87  88  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.43     1.19    3.62    1.13    \n",
      "Aalto__Dataset  88  89  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.18     1.16    3.35    1.15    \n",
      "Aalto__Dataset  89  90  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.27     1.24    3.51    1.18    \n",
      "Aalto__Dataset  90  91  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.57     1.22    3.79    1.32    \n",
      "Aalto__Dataset  91  92  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.86     1.29    4.16    1.3     \n",
      "Aalto__Dataset  92  93  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.36     1.18    3.53    1.02    \n",
      "Aalto__Dataset  93  94  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.22     1.17    3.39    1.01    \n",
      "Aalto__Dataset  94  95  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.36     1.18    3.54    1.13    \n",
      "Aalto__Dataset  95  96  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.42     1.26    3.68    1.13    \n",
      "Aalto__Dataset  96  97  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.59     1.22    3.81    1.12    \n",
      "Aalto__Dataset  97  98  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.66     1.29    3.95    1.24    \n",
      "Aalto__Dataset  98  99  Random  0.95  0.94  0.97  0.94  0.95  0.95  2.38     1.21    3.59    1.39    \n",
      "Aalto__Dataset  99  100 Random  0.95  0.94  0.97  0.94  0.95  0.95  2.41     1.4     3.81    1.32    \n",
      "                   precision    recall  f1-score       support\n",
      "Aria                0.892776  0.863000  0.877635   3000.000000\n",
      "D-LinkCam           0.985736  0.991990  0.988853   3000.000000\n",
      "D-LinkDayCam        0.977508  0.928000  0.952111   3000.000000\n",
      "D-LinkDevice        0.990782  0.896400  0.941225   3000.000000\n",
      "D-Sensors           0.978698  0.988108  0.983380  12000.000000\n",
      "EdimaxCam           0.993728  0.942583  0.967477   3000.000000\n",
      "EdimaxPlug          0.987136  0.944200  0.965190   6000.000000\n",
      "EdnetCam            0.985943  0.960000  0.972799   3000.000000\n",
      "EdnetGateway        0.989884  0.875450  0.929143   3000.000000\n",
      "HomeMaticPlug       1.000000  0.997000  0.998498   3000.000000\n",
      "Hue-Device          0.998914  0.999000  0.998957   3000.000000\n",
      "Lightify            1.000000  0.982570  0.991208   3000.000000\n",
      "MAXGateway          0.998582  0.939000  0.967875   3000.000000\n",
      "TP-LinkPlugHS       0.987309  0.968050  0.977583   6000.000000\n",
      "WeMoInsightSwitch   0.981686  0.921757  0.950776   3000.000000\n",
      "WeMoLink            0.986068  0.975390  0.980698   3000.000000\n",
      "WeMoSwitch          0.963405  0.924877  0.943745   3000.000000\n",
      "Withings            0.999084  0.849400  0.918173   3000.000000\n",
      "smart_Kettle        0.715092  0.999500  0.833708   6000.000000\n",
      "accuracy            0.952894  0.952894  0.952894      0.952894\n",
      "macro avg           0.969070  0.944541  0.954686  75000.000000\n",
      "weighted avg        0.961518  0.952894  0.954626  75000.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ths = open(\"1_08_2020_merged_3.csv\", \"w\")\n",
    "ths.write(\"Dataset,T,CV,ML algorithm,Acc,b_Acc,Precision, Recall , F1-score, kappa ,tra-Time,test-Time,total-Time),Al-Time)\\n\")\n",
    "repetition=100\n",
    "\n",
    "\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.preprocessing import Normalizer\n",
    "print ('%-15s %-3s %-3s %-6s  %-5s %-5s %-5s %-5s %-5s %-5s %-8s %-8s%-8s%-8s'%\n",
    "           (\"Dataset\",\"T\",\"CV\",\"ML alg\",\"Acc\",\"b_Acc\",\"Prec\", \"Rec\" , \"F1\", \"kap\" ,\"tra-T\",\"test-T\",\"total\",\"al-time\"))\n",
    "\n",
    "for ii in ml_list:\n",
    "    class_based_results=pd.DataFrame()#\"\" #pd.DataFrame(0, index=np.arange((len(target_names)+3)), columns=[\"f1-score\",\"precision\",\"recall\",\"support\"])\n",
    "    cm=pd.DataFrame()\n",
    "    cv=0\n",
    "    for i in range(repetition):\n",
    "\n",
    "\n",
    "        #TRAIN\n",
    "        df = pd.read_csv(loop)\n",
    "        df=df.replace({\"Label\": new_labels})\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)#,header=None )\n",
    "        df=df.replace({\"Label\": new_labels})\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",
    "        dname=loop  \n",
    "        results_y=[]\n",
    "        cv+=1\n",
    "        results_y.append(y_test)\n",
    "\n",
    "\n",
    "        precision=[]\n",
    "        recall=[]\n",
    "        f1=[]\n",
    "        accuracy=[]\n",
    "        train_time=[]\n",
    "        test_time=[]\n",
    "        total_time=[]\n",
    "        kappa=[]\n",
    "        accuracy_b=[]\n",
    "\n",
    "            #machine learning algorithm is applied in this section\n",
    "        clf = ml_list[ii]#choose algorithm from ml_list dictionary\n",
    "        second=time.time()\n",
    "        clf.fit(X_train, y_train)\n",
    "        train_time.append(float((time.time()-second)) )\n",
    "        second=time.time()\n",
    "        predict =clf.predict(X_test)\n",
    "        test_time.append(float((time.time()-second)) )\n",
    "        #normal\n",
    "        #lines,class_based_results=score(train_time,test_time,predict,y_test,class_based_results)\n",
    "        #merged\n",
    "        predict,altime=merged(m_test,y_test,predict)\n",
    "        lines,class_based_results=score(altime,train_time,test_time,predict,y_test,class_based_results)\n",
    "\n",
    "        ths.write (lines)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        df_cm = pd.DataFrame(confusion_matrix(y_test, predict))\n",
    "        results_y.append(predict)\n",
    "        if cm.empty:\n",
    "            cm =df_cm\n",
    "        else:\n",
    "            cm = cm.add(df_cm, fill_value=0)\n",
    "\n",
    "\n",
    "print(class_based_results/(repetition)) \n",
    "graph_name=\"merged_confusion matrix\" +str(ii)+\".pdf\"     \n",
    "plt.figure(figsize = (20,14))\n",
    "sns.heatmap(cm,xticklabels=target_names, yticklabels=target_names, annot=True)\n",
    "plt.savefig(graph_name,bbox_inches='tight')#, dpi=400)\n",
    "plt.show()\n",
    "#print(cm)\n",
    "print(\"\\n\\n\\n\") \n",
    "ths.close()  "
   ]
  }
 ],
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   "file_extension": ".py",
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