Total_Power_of_Node - Copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Tr_centrality_measure\n",
    "import Graph_Entropy_measure\n",
    "import Selection_Algorithm\n",
    "import Constraint_Coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from datetime import timedelta\n",
    "\n",
    "start_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "PI_table1 = pd.DataFrame(Graph_Entropy_measure.PI_Rank, columns = ('PI','PI_i'))\n",
    "#PI_table1.PI_i[PI_table.PI_i  < 0] = 0\n",
    "PI_table = PI_table1.copy()\n",
    "i = math.sqrt(2)\n",
    "PI_table.PI_i = PI_table.PI_i.where(lambda x: x > 0, lambda x: x + i)\n",
    "TC_table = pd.DataFrame(Tr_centrality_measure.TC_Rank, columns = ('TC','TC_i'))\n",
    "#TC_table.TC = TC_table.TC + 1\n",
    "CC_table = pd.DataFrame(Constraint_Coefficient.sorted_x, columns = ('CC','CC_i'))\n",
    "\n",
    "#CC_table.CC= CC_table.CC + 1\n",
    "Result_table = pd.concat([TC_table, PI_table, CC_table], axis=1, sort=False)\n",
    "\n",
    "First_Five= pd.DataFrame(Result_table.loc[0:2])\n",
    "#First_Five.index = First_Five.index + 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TC</th>\n",
       "      <th>TC_i</th>\n",
       "      <th>PI</th>\n",
       "      <th>PI_i</th>\n",
       "      <th>CC</th>\n",
       "      <th>CC_i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.4219</td>\n",
       "      <td>0</td>\n",
       "      <td>0.047863</td>\n",
       "      <td>0</td>\n",
       "      <td>0.155423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>0.4138</td>\n",
       "      <td>33</td>\n",
       "      <td>0.054788</td>\n",
       "      <td>33</td>\n",
       "      <td>0.156419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>32</td>\n",
       "      <td>0.062682</td>\n",
       "      <td>2</td>\n",
       "      <td>0.185177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TC    TC_i  PI      PI_i  CC      CC_i\n",
       "0   0  0.4219   0  0.047863   0  0.155423\n",
       "1  33  0.4138  33  0.054788  33  0.156419\n",
       "2  32  0.4000  32  0.062682   2  0.185177"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "First_Five"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 33, 32, 2]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes1 = list(pd.concat([First_Five['TC'], First_Five['PI'], First_Five['CC']]).unique())\n",
    "nodes1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#return the total power of node i.\n",
    "def TPN(i):\n",
    "    PI_ofi = 1/Graph_Entropy_measure.PI_of(i)\n",
    "    TC_ofi = Tr_centrality_measure.TC_of(i)\n",
    "    CC_ofi = Constraint_Coefficient.CC_of(i)\n",
    "    #Weight to normalize the measures\n",
    "    dn=PI_ofi+TC_ofi+CC_ofi\n",
    "    n1=round(TC_ofi/dn, 2)\n",
    "    n2=round(PI_ofi/dn, 2)\n",
    "    n3=round(CC_ofi/dn, 2)\n",
    "    #equation []\n",
    "    TPN_i  = (n1*TC_ofi)+(n2*PI_ofi)+(n3*CC_ofi)\n",
    "    #TPN_i  = (n1*TC_ofi)+(n2*PI_ofi)+(n3*CC_ofi)       \n",
    "\n",
    "    return TPN_i\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loop total power of node all i\n",
    "def loop_TPN():\n",
    "    i=0    \n",
    "    #nn=list(pd.core.common.intersection(First_Five['TC'], First_Five['PI'], First_Five['CC']))\n",
    "    nn = list(pd.concat([First_Five['TC'], First_Five['PI'], First_Five['CC']]).unique())\n",
    "    list_TPN=[]    \n",
    "    for i in nn:          \n",
    "        TPN_ = TPN(i)\n",
    "        i_TPN = [i, TPN_]\n",
    "        list_TPN.append(i_TPN)\n",
    "    return  list_TPN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ranking the total power of node i\n",
    "def TPN_Ranking( val ):\n",
    "      return val [1]\n",
    "\n",
    "TPN_Rank = loop_TPN()\n",
    "TPN_Rank.sort(key=lambda elem: elem[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TPN</th>\n",
       "      <th>TPN_i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.325341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>0.317139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>0.316870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0.274195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TPN     TPN_i\n",
       "0    0  0.325341\n",
       "1   33  0.317139\n",
       "2   32  0.316870\n",
       "3    2  0.274195"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#return table containing the TPN values[TPN_table and Top_Five]\n",
    "TPN = pd.DataFrame(TPN_Rank, columns = ('TPN','TPN_i'))\n",
    "#TPN.TPN = TPN.TPN + 1\n",
    "Top_TPN = pd.DataFrame(TPN.loc[0:4])\n",
    "\n",
    "TPN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   TC  PI  CC   TPN\n",
      "0   0   0   0   0.0\n",
      "1  33  33  33  33.0\n",
      "2  32  32   2  32.0\n",
      "3   1   1  31   2.0\n",
      "4   2   2  32   NaN\n"
     ]
    }
   ],
   "source": [
    "#return table containing ALL the Measures\n",
    "Propose_Result_table = pd.concat([TC_table, PI_table, CC_table, TPN], axis=1, sort=False)\n",
    "Top_three = pd.DataFrame(Propose_Result_table.loc[0:4])\n",
    "#return table containing top three node ranking\n",
    "top_rank_table = pd.concat([Top_three['TC'], Top_three['PI'], \n",
    "                           Top_three['CC'],Top_three['TPN']], \n",
    "                            axis=1, sort=False)\n",
    "print(top_rank_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result_table.to_csv('result/kc--/propose/propose_result_table_kc.csv')#save the value of (TC,TC_i),(PI,PI_i),(CC,CC_i)\n",
    "#top_rank_table.to_csv('result/kc--/propose/top_rank_table_kc.csv')#save top three of(TC,PI,CC,TPN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution took: 0:00:01 secs (Wall clock time)\n"
     ]
    }
   ],
   "source": [
    "elapsed_time_secs = time.time() - start_time\n",
    "\n",
    "msg = \"Execution took: %s secs (Wall clock time)\" % timedelta(seconds=round(elapsed_time_secs))\n",
    "\n",
    "print(msg)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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