main_trust_data_generation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#this program will generate the trust table based on frequency, intimacy and recency\n",
    "#our mobile app is capable of generating these separately\n",
    "#use this data for clustering in 3 classes [trusted, spam, unspecified or new number entry] to train your perceptron\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandasql as pdsql\n",
    "pysql = lambda q: pdsql.sqldf(q, globals())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url1='generated/call_log.csv'\n",
    "url2='generated/user_tendency.csv'\n",
    "call_log = pd.read_csv(url1)\n",
    "user_tendency=pd.read_csv(url2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class CallDetails:\n",
    "    in_dur=0\n",
    "    out_dur=0\n",
    "    in_f=0\n",
    "    out_f=0\n",
    "    date=0\n",
    "    def __init__(self,no='9999999999',indur=0,outdur=0,inf=0,outf=0,dt=0):\n",
    "        self.to_number=no\n",
    "        self.in_dur=indur\n",
    "        self.out_dur=outdur\n",
    "        self.in_f=inf\n",
    "        self.out_f=outf\n",
    "        self.date=dt\n",
    "        \n",
    "class UserDetails:\n",
    "    tot_durin=0\n",
    "    tot_durout=0\n",
    "    tendency=0\n",
    "    tot_inf=0\n",
    "    tot_outf=0\n",
    "    def __init__(self,no='9999999999',din=0,dout=0,cin=0,cout=0,tend=0):\n",
    "        self.tot_durin=din\n",
    "        self.tot_durout=dout\n",
    "        self.tendency=tend\n",
    "        self.tot_inf=cin\n",
    "        self.tot_outf=cout\n",
    "        self.number=no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6553"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of unique numbers\n",
    "no_rows=user_tendency.shape[0]\n",
    "users=dict()\n",
    "#storing per user details for future reference\n",
    "for i in range(no_rows):\n",
    "    callingno=  str(user_tendency.loc[i]['NUMBER']).split('.')[0]\n",
    "    tot_in=user_tendency.loc[i]['TOT_IN_CALLS']\n",
    "    tot_out=user_tendency.loc[i]['TOT_OUT_CALLS']\n",
    "    indur=user_tendency.loc[i]['TOT_IN_DUR']\n",
    "    outdur=user_tendency.loc[i]['TOT_OUT_DUR']\n",
    "    tendy=user_tendency.loc[i]['TENDENCY']\n",
    "    users[callingno]=UserDetails(callingno,indur,outdur,tot_in,tot_out,tendy)\n",
    "len(users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6551"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calldb will contain the stats of all calls made from one number to another \n",
    "calldb=dict()\n",
    "query=(\"select * from call_log where RECORD_TYPE<>\\\"SMS\\\" ORDER BY CALL_DATE DESC;\")\n",
    "allcalls=pysql(query)\n",
    "dformat='%d-%m-%Y  %H:%M'\n",
    "no_rows=allcalls.shape[0]\n",
    "for i in range(no_rows):\n",
    "    callingno=str(allcalls.loc[i]['CALLING_NO'])\n",
    "    calledno=str(allcalls.loc[i]['CALLED_NO'])\n",
    "    if not (calledno =='5.43E+11' or callingno=='4.47E+11'):\n",
    "        dur=allcalls.loc[i]['DURATION']\n",
    "        #direction=allcalls.loc[i]['DIRECTION']\n",
    "        ts = time.strptime(allcalls.loc[i]['CALL_DATE'],dformat )\n",
    "        date= time.mktime(ts)\n",
    "        if callingno not in calldb:\n",
    "            calldb[callingno]=dict()\n",
    "        if calledno not in calldb:\n",
    "            calldb[calledno]=dict()\n",
    "        outdur=0\n",
    "        indur=0\n",
    "        cin=0\n",
    "        cout=0\n",
    "        if calledno not in calldb[callingno]:\n",
    "            calldb[callingno][calledno]=CallDetails(calledno,indur,outdur,cin,cout,date)\n",
    "        if callingno not in calldb[calledno]:\n",
    "            calldb[calledno][callingno]=CallDetails(callingno,indur,outdur,cin,cout,date)\n",
    "        #note we dont update date since its being fed in date desc order\n",
    "        #update sender creds\n",
    "        calldb[callingno][calledno].out_dur+=dur\n",
    "        calldb[callingno][calledno].out_f+=1\n",
    "        #update receiver creds\n",
    "        calldb[calledno][callingno].in_dur+=dur\n",
    "        calldb[calledno][callingno].in_f+=1\n",
    "    \n",
    "len(calldb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "ename": "PicklingError",
     "evalue": "Can't pickle <class '__main__.CallDetails'>: it's not the same object as __main__.CallDetails",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPicklingError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-73-3e8324235fda>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpickle\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpkl\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mpkl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mcalldb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;34m\"calldb.pkl\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"wb\"\u001b[0m \u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mPicklingError\u001b[0m: Can't pickle <class '__main__.CallDetails'>: it's not the same object as __main__.CallDetails"
     ]
    }
   ],
   "source": [
    "import pickle as pkl\n",
    "pkl.dump( calldb, open( \"generated/calldb.pkl\", \"wb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CALLING_NO</th>\n",
       "      <th>CALLED_NO</th>\n",
       "      <th>FREQUENCY</th>\n",
       "      <th>INTIMACY</th>\n",
       "      <th>RECENCY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9719974132</td>\n",
       "      <td>9045999933</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.725495</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9719974132</td>\n",
       "      <td>0.038591</td>\n",
       "      <td>0.013958</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9319603547</td>\n",
       "      <td>0.008389</td>\n",
       "      <td>0.002735</td>\n",
       "      <td>0.999802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9319444005</td>\n",
       "      <td>0.016779</td>\n",
       "      <td>0.008095</td>\n",
       "      <td>0.998467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9219723353</td>\n",
       "      <td>0.008389</td>\n",
       "      <td>0.001480</td>\n",
       "      <td>0.997231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9896203763</td>\n",
       "      <td>0.013423</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.996588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9760587687</td>\n",
       "      <td>0.010067</td>\n",
       "      <td>0.006180</td>\n",
       "      <td>0.995302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>7500922332</td>\n",
       "      <td>0.031879</td>\n",
       "      <td>0.014766</td>\n",
       "      <td>0.993671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9358152053</td>\n",
       "      <td>0.001678</td>\n",
       "      <td>0.004895</td>\n",
       "      <td>0.993374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9758417306</td>\n",
       "      <td>0.104027</td>\n",
       "      <td>0.029146</td>\n",
       "      <td>0.993324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9610633366</td>\n",
       "      <td>0.011745</td>\n",
       "      <td>0.007454</td>\n",
       "      <td>0.99283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9634646681</td>\n",
       "      <td>0.001678</td>\n",
       "      <td>0.001621</td>\n",
       "      <td>0.961183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9950433389</td>\n",
       "      <td>0.015101</td>\n",
       "      <td>0.016133</td>\n",
       "      <td>0.960095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9359202692</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.002262</td>\n",
       "      <td>0.959947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9255962004</td>\n",
       "      <td>0.003356</td>\n",
       "      <td>0.007346</td>\n",
       "      <td>0.958958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9319903867</td>\n",
       "      <td>0.003356</td>\n",
       "      <td>0.001172</td>\n",
       "      <td>0.958513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9457649097</td>\n",
       "      <td>0.001678</td>\n",
       "      <td>0.004328</td>\n",
       "      <td>0.957771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9897080661</td>\n",
       "      <td>0.001678</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.956782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9997015338</td>\n",
       "      <td>0.005034</td>\n",
       "      <td>0.005021</td>\n",
       "      <td>0.956188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9690443725</td>\n",
       "      <td>0.036913</td>\n",
       "      <td>0.014793</td>\n",
       "      <td>0.954161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>8979778013</td>\n",
       "      <td>0.015101</td>\n",
       "      <td>0.012399</td>\n",
       "      <td>0.953667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9557571175</td>\n",
       "      <td>0.005034</td>\n",
       "      <td>0.000740</td>\n",
       "      <td>0.952875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9359735561</td>\n",
       "      <td>0.013423</td>\n",
       "      <td>0.005904</td>\n",
       "      <td>0.949315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9410689488</td>\n",
       "      <td>0.001678</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>0.948771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>8979778011</td>\n",
       "      <td>0.011745</td>\n",
       "      <td>0.002544</td>\n",
       "      <td>0.947881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9760170000</td>\n",
       "      <td>0.003356</td>\n",
       "      <td>0.000730</td>\n",
       "      <td>0.942195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9358193884</td>\n",
       "      <td>0.008389</td>\n",
       "      <td>0.005444</td>\n",
       "      <td>0.941107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9375324319</td>\n",
       "      <td>0.013423</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>0.941057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9896145089</td>\n",
       "      <td>0.008389</td>\n",
       "      <td>0.005881</td>\n",
       "      <td>0.940563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>9045999933</td>\n",
       "      <td>9672837712</td>\n",
       "      <td>0.005034</td>\n",
       "      <td>0.004766</td>\n",
       "      <td>0.939524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13376</th>\n",
       "      <td>8865849159</td>\n",
       "      <td>8791759561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13377</th>\n",
       "      <td>8869084227</td>\n",
       "      <td>8791759561</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13378</th>\n",
       "      <td>9258135110</td>\n",
       "      <td>9045061506</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13379</th>\n",
       "      <td>9760344044</td>\n",
       "      <td>8439000055</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13380</th>\n",
       "      <td>8439817013</td>\n",
       "      <td>9045849930</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13381</th>\n",
       "      <td>9812237232</td>\n",
       "      <td>9045042345</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13382</th>\n",
       "      <td>9411264041</td>\n",
       "      <td>9045060007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13383</th>\n",
       "      <td>8881511910</td>\n",
       "      <td>9045112200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.749135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13384</th>\n",
       "      <td>9690012106</td>\n",
       "      <td>9045091352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13385</th>\n",
       "      <td>9829167468</td>\n",
       "      <td>9045999933</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13386</th>\n",
       "      <td>8950545601</td>\n",
       "      <td>9045004100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13387</th>\n",
       "      <td>9557323518</td>\n",
       "      <td>9045849930</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13388</th>\n",
       "      <td>9760716797</td>\n",
       "      <td>8791777788</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13389</th>\n",
       "      <td>9319068931</td>\n",
       "      <td>9045091352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13390</th>\n",
       "      <td>9214423797</td>\n",
       "      <td>9045999933</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13391</th>\n",
       "      <td>9760708185</td>\n",
       "      <td>7417467410</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13392</th>\n",
       "      <td>9634146810</td>\n",
       "      <td>9045849930</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13393</th>\n",
       "      <td>9719190431</td>\n",
       "      <td>9045091352</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.569520</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13394</th>\n",
       "      <td>9368855905</td>\n",
       "      <td>9045033375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13395</th>\n",
       "      <td>9411627467</td>\n",
       "      <td>9045096882</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13396</th>\n",
       "      <td>9412578632</td>\n",
       "      <td>9045849930</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13397</th>\n",
       "      <td>9997162046</td>\n",
       "      <td>7417265215</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13398</th>\n",
       "      <td>8601466320</td>\n",
       "      <td>9045091352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13399</th>\n",
       "      <td>1144600600</td>\n",
       "      <td>8791759561</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13400</th>\n",
       "      <td>8791275669</td>\n",
       "      <td>9027660333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13401</th>\n",
       "      <td>9359440830</td>\n",
       "      <td>9045004100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13402</th>\n",
       "      <td>7417172694</td>\n",
       "      <td>9045033379</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.740733</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13403</th>\n",
       "      <td>9536548683</td>\n",
       "      <td>9045098851</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13404</th>\n",
       "      <td>9045229800</td>\n",
       "      <td>8791001055</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13405</th>\n",
       "      <td>7417662933</td>\n",
       "      <td>9045003373</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13406 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CALLING_NO   CALLED_NO  FREQUENCY  INTIMACY   RECENCY\n",
       "0      9719974132  9045999933   1.000000  0.725495         1\n",
       "1      9045999933  9719974132   0.038591  0.013958         1\n",
       "2      9045999933  9319603547   0.008389  0.002735  0.999802\n",
       "3      9045999933  9319444005   0.016779  0.008095  0.998467\n",
       "4      9045999933  9219723353   0.008389  0.001480  0.997231\n",
       "5      9045999933  9896203763   0.013423  0.024961  0.996588\n",
       "6      9045999933  9760587687   0.010067  0.006180  0.995302\n",
       "7      9045999933  7500922332   0.031879  0.014766  0.993671\n",
       "8      9045999933  9358152053   0.001678  0.004895  0.993374\n",
       "9      9045999933  9758417306   0.104027  0.029146  0.993324\n",
       "10     9045999933  9610633366   0.011745  0.007454   0.99283\n",
       "11     9045999933  9634646681   0.001678  0.001621  0.961183\n",
       "12     9045999933  9950433389   0.015101  0.016133  0.960095\n",
       "13     9045999933  9359202692   0.006711  0.002262  0.959947\n",
       "14     9045999933  9255962004   0.003356  0.007346  0.958958\n",
       "15     9045999933  9319903867   0.003356  0.001172  0.958513\n",
       "16     9045999933  9457649097   0.001678  0.004328  0.957771\n",
       "17     9045999933  9897080661   0.001678  0.000144  0.956782\n",
       "18     9045999933  9997015338   0.005034  0.005021  0.956188\n",
       "19     9045999933  9690443725   0.036913  0.014793  0.954161\n",
       "20     9045999933  8979778013   0.015101  0.012399  0.953667\n",
       "21     9045999933  9557571175   0.005034  0.000740  0.952875\n",
       "22     9045999933  9359735561   0.013423  0.005904  0.949315\n",
       "23     9045999933  9410689488   0.001678  0.000473  0.948771\n",
       "24     9045999933  8979778011   0.011745  0.002544  0.947881\n",
       "25     9045999933  9760170000   0.003356  0.000730  0.942195\n",
       "26     9045999933  9358193884   0.008389  0.005444  0.941107\n",
       "27     9045999933  9375324319   0.013423  0.006567  0.941057\n",
       "28     9045999933  9896145089   0.008389  0.005881  0.940563\n",
       "29     9045999933  9672837712   0.005034  0.004766  0.939524\n",
       "...           ...         ...        ...       ...       ...\n",
       "13376  8865849159  8791759561   1.000000  1.000000         1\n",
       "13377  8869084227  8791759561   0.000000  1.000000         1\n",
       "13378  9258135110  9045061506   1.000000  1.000000         1\n",
       "13379  9760344044  8439000055   0.000000  1.000000         1\n",
       "13380  8439817013  9045849930   1.000000  1.000000         1\n",
       "13381  9812237232  9045042345   0.000000  1.000000         1\n",
       "13382  9411264041  9045060007   0.000000  1.000000         1\n",
       "13383  8881511910  9045112200   1.000000  0.749135         1\n",
       "13384  9690012106  9045091352   0.000000  1.000000         1\n",
       "13385  9829167468  9045999933   1.000000  1.000000         1\n",
       "13386  8950545601  9045004100   0.000000  1.000000         1\n",
       "13387  9557323518  9045849930   1.000000  1.000000         1\n",
       "13388  9760716797  8791777788   1.000000  1.000000         1\n",
       "13389  9319068931  9045091352   0.000000  1.000000         1\n",
       "13390  9214423797  9045999933   0.000000  1.000000         1\n",
       "13391  9760708185  7417467410   0.000000  1.000000         1\n",
       "13392  9634146810  9045849930   1.000000  1.000000         1\n",
       "13393  9719190431  9045091352   1.000000  0.569520         1\n",
       "13394  9368855905  9045033375   0.000000  1.000000         1\n",
       "13395  9411627467  9045096882   0.000000  1.000000         1\n",
       "13396  9412578632  9045849930   1.000000  1.000000         1\n",
       "13397  9997162046  7417265215   0.000000  1.000000         1\n",
       "13398  8601466320  9045091352   0.000000  1.000000         1\n",
       "13399  1144600600  8791759561   0.000000  1.000000         1\n",
       "13400  8791275669  9027660333   1.000000  1.000000         1\n",
       "13401  9359440830  9045004100   0.000000  1.000000         1\n",
       "13402  7417172694  9045033379   1.000000  0.740733         1\n",
       "13403  9536548683  9045098851   0.000000  1.000000         1\n",
       "13404  9045229800  8791001055   0.000000  1.000000         1\n",
       "13405  7417662933  9045003373   0.000000  1.000000         1\n",
       "\n",
       "[13406 rows x 5 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "#create new dataframe\n",
    "columns = ['CALLING_NO','CALLED_NO','FREQUENCY','INTIMACY', 'RECENCY']\n",
    "#tendency= total out duration/total duration\n",
    "trustdb = pd.DataFrame(columns=columns)\n",
    "i=0\n",
    "for callingno in calldb.keys():\n",
    "    mp=-1\n",
    "    lp=sys.float_info.max\n",
    "    for calledno in calldb[callingno].keys():\n",
    "        if mp<calldb[callingno][calledno].date:\n",
    "            mp=calldb[callingno][calledno].date\n",
    "        if lp>calldb[callingno][calledno].date:\n",
    "            lp=calldb[callingno][calledno].date   \n",
    "    #period of consideration=(timestamp(most_recent_user)-timestamp(least_recent_user))\n",
    "    period=mp-lp\n",
    "    for calledno in calldb[callingno].keys():\n",
    "        frequency,intimacy=0,0\n",
    "\n",
    "        #frequency=#out_calls_to_that_person/#out_calls_to_everyone\n",
    "        if users[callingno].tot_outf !=0:\n",
    "            frequency=float(calldb[callingno][calledno].out_f)/users[callingno].tot_outf\n",
    "            \n",
    "        #intimacy=tendency*outgoingcalldur/totdur +(1-tendency)*incomingcalldur/totdur\n",
    "        totdur=users[callingno].tot_durin+users[callingno].tot_durout\n",
    "        if totdur !=0:\n",
    "            c=float(calldb[callingno][calledno].out_dur)\n",
    "            d=float(calldb[callingno][calledno].in_dur)\n",
    "            tendency=users[callingno].tendency\n",
    "            intimacy=tendency*c/totdur + (1-tendency)*d/totdur\n",
    "        #recency=(timestamp(most_recent_user_person)-timestamp(least_recent_user))/total_period_of_consideration\n",
    "        u_p=calldb[callingno][calledno].date\n",
    "        recency=1\n",
    "        if period!=0:\n",
    "            recency=(u_p-lp)/period\n",
    "        trustdb.loc[i]=[callingno,calledno,frequency,intimacy,recency]\n",
    "        i+=1\n",
    "trustdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trustdb.to_csv('train/trustdb.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url3='train/trustdb.csv'\n",
    "trustdb = pd.read_csv(url3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#for normalization in 0 to 1 use this, but this aint doing good normalization \n",
    "def normalize(df):\n",
    "    result = df.copy()\n",
    "    for feature_name in df.columns:\n",
    "        max_value = df[feature_name].max()\n",
    "        min_value = df[feature_name].min()\n",
    "        result[feature_name] = (df[feature_name] - min_value) / (max_value - min_value)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trustdb=trustdb.drop(['CALLING_NO','CALLED_NO'],axis=1)\n",
    "mean=trustdb.mean()\n",
    "sd=trustdb.std()\n",
    "trustdb_norm=(trustdb-mean)/sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nfig = plt.figure()\\nax = fig.add_subplot(111)\\nscatter = ax.scatter(trustdb['FREQUENCY'],trustdb['INTIMACY'],c=Cluster,s=50)\\nfor frequency,recency in centers:\\n    ax.scatter(frequency,recency,s=50,c='red',marker='+')\\nax.set_xlabel('frequency')\\nax.set_ylabel('intimacy')\\nplt.colorbar(scatter)\\nfig.show()\\n\""
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "kmeans = KMeans(n_clusters=4, random_state=0).fit(trustdb_norm.values)\n",
    "centers = kmeans.cluster_centers_\n",
    "Cluster=kmeans.predict(trustdb_norm)\n",
    "'''\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "scatter = ax.scatter(trustdb['FREQUENCY'],trustdb['INTIMACY'],c=Cluster,s=50)\n",
    "for frequency,recency in centers:\n",
    "    ax.scatter(frequency,recency,s=50,c='red',marker='+')\n",
    "ax.set_xlabel('frequency')\n",
    "ax.set_ylabel('intimacy')\n",
    "plt.colorbar(scatter)\n",
    "fig.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aFiuVQDUjQ0ofp3QGVWmzPJZlDberpx6o8Lu5sFgs6OzslAqyxXE40WgUy8vL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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2030c33ec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fignum = 1\n",
    "fig = plt.figure(fignum, figsize=(10, 10))\n",
    "\n",
    "#ax = Axes3D(fig, rect=[0, 0, 2, 2], elev=48, azim=134)\n",
    "# Placement 0, 0 would be the bottom left, 1, 1 would be the top right.\n",
    "#ax.text2D(0, 0, \"TRUST CLUSTERS\", transform=ax.transAxes)\n",
    "ax = fig.gca(projection='3d')\n",
    "\n",
    "labels = kmeans.labels_\n",
    "colors=['red','green','blue','black']\n",
    "col_map=dict(zip(set(labels),colors))\n",
    "label_color = [col_map[l] for l in labels]\n",
    "plt.scatter(trustdb['FREQUENCY'], trustdb['INTIMACY'], trustdb['RECENCY'], c=label_color)\n",
    "\n",
    "ax.set_xlabel('FREQUENCY')\n",
    "ax.set_ylabel('INTIMACY')\n",
    "ax.set_zlabel('RECENCY')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FREQUENCY    0.254138\n",
      "INTIMACY     0.438586\n",
      "RECENCY      0.779383\n",
      "dtype: float64\n",
      "FREQUENCY    0.422264\n",
      "INTIMACY     0.458369\n",
      "RECENCY      0.314565\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(mean)\n",
    "print(sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trust_results=kmeans.predict(trustdb)\n",
    "trustY = pd.DataFrame({'trust': trust_results})\n",
    "trustdb1=trustdb.join(trustY)\n",
    "trustdb1.to_csv('train/trustdb_gaussian.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query0=\"SELECT * FROM trustdb1 WHERE trust=0;\"#HIGH\n",
    "trust0=pysql(query0)\n",
    "query1=\"SELECT * FROM trustdb1 WHERE trust=1;\"#SPAM\n",
    "trust1=pysql(query1)\n",
    "query2=\"SELECT * FROM trustdb1 WHERE trust=2;\"#longDistanceRelationship low frequency just\n",
    "trust2=pysql(query2)\n",
    "query3=\"SELECT * FROM trustdb1 WHERE trust=3;\"#recent, not intimate, below avg frequent\n",
    "trust3=pysql(query3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FREQUENCY</th>\n",
       "      <th>INTIMACY</th>\n",
       "      <th>RECENCY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.725495</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.038591</td>\n",
       "      <td>0.013958</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.008389</td>\n",
       "      <td>0.002735</td>\n",
       "      <td>0.999802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.016779</td>\n",
       "      <td>0.008095</td>\n",
       "      <td>0.998467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.008389</td>\n",
       "      <td>0.001480</td>\n",
       "      <td>0.997231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.013423</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.996588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.010067</td>\n",
       "      <td>0.006180</td>\n",
       "      <td>0.995302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.031879</td>\n",
       "      <td>0.014766</td>\n",
       "      <td>0.993671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.001678</td>\n",
       "      <td>0.004895</td>\n",
       "      <td>0.993374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.104027</td>\n",
       "      <td>0.029146</td>\n",
       "      <td>0.993324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.011745</td>\n",
       "      <td>0.007454</td>\n",
       "      <td>0.992830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.001678</td>\n",
       "      <td>0.001621</td>\n",
       "      <td>0.961183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.015101</td>\n",
       "      <td>0.016133</td>\n",
       "      <td>0.960095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.002262</td>\n",
       "      <td>0.959947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.003356</td>\n",
       "      <td>0.007346</td>\n",
       "      <td>0.958958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.003356</td>\n",
       "      <td>0.001172</td>\n",
       "      <td>0.958513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.001678</td>\n",
       "      <td>0.004328</td>\n",
       "      <td>0.957771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.001678</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.956782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.005034</td>\n",
       "      <td>0.005021</td>\n",
       "      <td>0.956188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.036913</td>\n",
       "      <td>0.014793</td>\n",
       "      <td>0.954161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.015101</td>\n",
       "      <td>0.012399</td>\n",
       "      <td>0.953667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.005034</td>\n",
       "      <td>0.000740</td>\n",
       "      <td>0.952875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.013423</td>\n",
       "      <td>0.005904</td>\n",
       "      <td>0.949315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.001678</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>0.948771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.011745</td>\n",
       "      <td>0.002544</td>\n",
       "      <td>0.947881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.003356</td>\n",
       "      <td>0.000730</td>\n",
       "      <td>0.942195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.008389</td>\n",
       "      <td>0.005444</td>\n",
       "      <td>0.941107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.013423</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>0.941057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.008389</td>\n",
       "      <td>0.005881</td>\n",
       "      <td>0.940563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.005034</td>\n",
       "      <td>0.004766</td>\n",
       "      <td>0.939524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>13376</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13377</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13378</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13379</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13380</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>13381</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13382</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>13383</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.749135</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>13384</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>13385</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13386</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13387</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13388</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>13389</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>13390</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13391</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13392</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13393</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.569520</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13394</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13395</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13396</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13397</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13398</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13399</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13400</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13401</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13402</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.740733</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13403</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13404</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13405</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13406 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FREQUENCY  INTIMACY   RECENCY\n",
       "0       1.000000  0.725495  1.000000\n",
       "1       0.038591  0.013958  1.000000\n",
       "2       0.008389  0.002735  0.999802\n",
       "3       0.016779  0.008095  0.998467\n",
       "4       0.008389  0.001480  0.997231\n",
       "5       0.013423  0.024961  0.996588\n",
       "6       0.010067  0.006180  0.995302\n",
       "7       0.031879  0.014766  0.993671\n",
       "8       0.001678  0.004895  0.993374\n",
       "9       0.104027  0.029146  0.993324\n",
       "10      0.011745  0.007454  0.992830\n",
       "11      0.001678  0.001621  0.961183\n",
       "12      0.015101  0.016133  0.960095\n",
       "13      0.006711  0.002262  0.959947\n",
       "14      0.003356  0.007346  0.958958\n",
       "15      0.003356  0.001172  0.958513\n",
       "16      0.001678  0.004328  0.957771\n",
       "17      0.001678  0.000144  0.956782\n",
       "18      0.005034  0.005021  0.956188\n",
       "19      0.036913  0.014793  0.954161\n",
       "20      0.015101  0.012399  0.953667\n",
       "21      0.005034  0.000740  0.952875\n",
       "22      0.013423  0.005904  0.949315\n",
       "23      0.001678  0.000473  0.948771\n",
       "24      0.011745  0.002544  0.947881\n",
       "25      0.003356  0.000730  0.942195\n",
       "26      0.008389  0.005444  0.941107\n",
       "27      0.013423  0.006567  0.941057\n",
       "28      0.008389  0.005881  0.940563\n",
       "29      0.005034  0.004766  0.939524\n",
       "...          ...       ...       ...\n",
       "13376   1.000000  1.000000  1.000000\n",
       "13377   0.000000  1.000000  1.000000\n",
       "13378   1.000000  1.000000  1.000000\n",
       "13379   0.000000  1.000000  1.000000\n",
       "13380   1.000000  1.000000  1.000000\n",
       "13381   0.000000  1.000000  1.000000\n",
       "13382   0.000000  1.000000  1.000000\n",
       "13383   1.000000  0.749135  1.000000\n",
       "13384   0.000000  1.000000  1.000000\n",
       "13385   1.000000  1.000000  1.000000\n",
       "13386   0.000000  1.000000  1.000000\n",
       "13387   1.000000  1.000000  1.000000\n",
       "13388   1.000000  1.000000  1.000000\n",
       "13389   0.000000  1.000000  1.000000\n",
       "13390   0.000000  1.000000  1.000000\n",
       "13391   0.000000  1.000000  1.000000\n",
       "13392   1.000000  1.000000  1.000000\n",
       "13393   1.000000  0.569520  1.000000\n",
       "13394   0.000000  1.000000  1.000000\n",
       "13395   0.000000  1.000000  1.000000\n",
       "13396   1.000000  1.000000  1.000000\n",
       "13397   0.000000  1.000000  1.000000\n",
       "13398   0.000000  1.000000  1.000000\n",
       "13399   0.000000  1.000000  1.000000\n",
       "13400   1.000000  1.000000  1.000000\n",
       "13401   0.000000  1.000000  1.000000\n",
       "13402   1.000000  0.740733  1.000000\n",
       "13403   0.000000  1.000000  1.000000\n",
       "13404   0.000000  1.000000  1.000000\n",
       "13405   0.000000  1.000000  1.000000\n",
       "\n",
       "[13406 rows x 3 columns]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trustdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6993 6413 0 0\n"
     ]
    }
   ],
   "source": [
    "red=0\n",
    "blue=0\n",
    "green=0\n",
    "black=0\n",
    "for i in labels:\n",
    "    if i==0 :\n",
    "        red+=1\n",
    "    if i==1 :\n",
    "        blue+=1\n",
    "    if i==2 :\n",
    "        green+=1\n",
    "    if i==3:\n",
    "        black+=1\n",
    "print(str(red)+\" \"+str(blue)+\" \"+str(green)+\" \"+str(black))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 2, 2, 2])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(labels)\n",
    "print('red'+' blue'+' green'+' black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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