鲁棒性分析.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import random as rd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "side_path = '公交线路-对偶.csv'\n",
    "dot_path = '公交站点-对偶.csv'\n",
    "#请用公交数据处理.py处理过的数据进行使用\n",
    "#只要格式与范例数据一样也可使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                        source                      target  Weight\n0        100路(草海公交首末站--小菜园立交桥)         101路(眠山车场--小菜园立交桥5)       3\n1        100路(草海公交首末站--小菜园立交桥)        104路(大观楼(大观路)--巴士家园)       1\n2        100路(草海公交首末站--小菜园立交桥)   113路(大漾田公交首末站--潘家湾(人民西路))       1\n3        100路(草海公交首末站--小菜园立交桥)          115路(西苑立交桥--蓝龙潭车场)       1\n4        100路(草海公交首末站--小菜园立交桥)           124路(艺术剧院--大观楼车场)       1\n...                        ...                         ...     ...\n1887          Z86路(眠山车场--眠山车场)            Z86路(眠山车场--眠山车场)       1\n1888      Z87路(马街--西山区政府(秀苑路))        Z113路(马街(春雨路)--马街中村)       1\n1889     Z8路(昆明南站西广场--雨花公交枢纽站)  Z15路(高新区管委会(马金铺)--雨花公交枢纽站)       1\n1890     Z8路(昆明南站西广场--雨花公交枢纽站)        Z26路(万峰街东口--雨花公交枢纽站)       1\n1891  Z92路(洛龙村(祥和街)--白龙潭淏园1号门)   Z105路(彩云南路口(朝云街)--众和康园南门)       1\n\n[1892 rows x 3 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\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>source</th>\n      <th>target</th>\n      <th>Weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>100路(草海公交首末站--小菜园立交桥)</td>\n      <td>101路(眠山车场--小菜园立交桥5)</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>100路(草海公交首末站--小菜园立交桥)</td>\n      <td>104路(大观楼(大观路)--巴士家园)</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>100路(草海公交首末站--小菜园立交桥)</td>\n      <td>113路(大漾田公交首末站--潘家湾(人民西路))</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>100路(草海公交首末站--小菜园立交桥)</td>\n      <td>115路(西苑立交桥--蓝龙潭车场)</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>100路(草海公交首末站--小菜园立交桥)</td>\n      <td>124路(艺术剧院--大观楼车场)</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1887</th>\n      <td>Z86路(眠山车场--眠山车场)</td>\n      <td>Z86路(眠山车场--眠山车场)</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1888</th>\n      <td>Z87路(马街--西山区政府(秀苑路))</td>\n      <td>Z113路(马街(春雨路)--马街中村)</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1889</th>\n      <td>Z8路(昆明南站西广场--雨花公交枢纽站)</td>\n      <td>Z15路(高新区管委会(马金铺)--雨花公交枢纽站)</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1890</th>\n      <td>Z8路(昆明南站西广场--雨花公交枢纽站)</td>\n      <td>Z26路(万峰街东口--雨花公交枢纽站)</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1891</th>\n      <td>Z92路(洛龙村(祥和街)--白龙潭淏园1号门)</td>\n      <td>Z105路(彩云南路口(朝云街)--众和康园南门)</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>1892 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "side1 = pd.read_csv(side_path, encoding='gbk')\n",
    "dot1 = pd.read_csv(dot_path, encoding='gbk')\n",
    "side_b = pd.DataFrame(side1)\n",
    "dot2 = pd.DataFrame(dot1)\n",
    "side_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "dot = list(dot2.iloc[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [],
   "source": [
    "def degree_averge(G):\n",
    "    degree = nx.degree_histogram(G)\n",
    "    i = 0\n",
    "    i1 = 0\n",
    "    i2 = 0\n",
    "    for s in degree:\n",
    "        i1 += i * s\n",
    "        i += 1\n",
    "        i2 += s\n",
    "    f = i1 / i2\n",
    "    return f\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [],
   "source": [
    "def attack(dot, side_b):\n",
    "    G1 = nx.Graph()\n",
    "\n",
    "    nodes = []\n",
    "    for i in dot:\n",
    "        a = rd.random()\n",
    "        if p <= a:\n",
    "            G1.add_node(i)\n",
    "\n",
    "            nodes.append(i)\n",
    "        else:\n",
    "            side_b = side_b[~side_b['source'].isin([i])]\n",
    "            side_b = side_b[~side_b['target'].isin([i])]\n",
    "    side_q = list(side_b.iloc[:, 0])\n",
    "    side_z = list(side_b.iloc[:, 1])\n",
    "    for i in range(0, len(side_z) - 1):\n",
    "        G1.add_edge(side_q[i], side_z[i])\n",
    "    return G1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [
    {
     "data": {
      "text/plain": "[0.25220292415332296,\n 0.2528191087743696,\n 0.2537668797629456,\n 0.25117438964446975,\n 0.2502382157031854,\n 0.2482374698305365,\n 0.24690367569549515,\n 0.24557625649098683,\n 0.24230270935748882,\n 0.23839570169600569,\n 0.23746074268444387,\n 0.23403772691757796,\n 0.23698447962196934,\n 0.23511406271874963,\n 0.23446284700737904,\n 0.2355156911627383,\n 0.23414649893051023,\n 0.23319822890946024,\n 0.2343804380991112,\n 0.23503891006402433,\n 0.23753351744174558,\n 0.23545040867028755,\n 0.2346836937082707,\n 0.23290394070224738,\n 0.2327103456919001,\n 0.23058915315716333,\n 0.22689257109889324,\n 0.22601254973496654,\n 0.22546484102042835,\n 0.22459510544155026,\n 0.2236235055325264,\n 0.2232113010672636,\n 0.2248993806023059,\n 0.22559307037577217,\n 0.22701021239166602,\n 0.22303631852877467,\n 0.2246362425805978,\n 0.2228367852867194,\n 0.2183289469508313,\n 0.21977135939853182,\n 0.21609034279517284,\n 0.2192258920331611,\n 0.22092059592065527,\n 0.22174289599723512,\n 0.22110334649861563,\n 0.21881696387424995,\n 0.21294854292783036,\n 0.21284768693053,\n 0.20700412681970692,\n 0.20643949570452005,\n 0.20390986458111404,\n 0.20083206494164954,\n 0.198617778053755,\n 0.20033603436106728,\n 0.19818946885721409,\n 0.19469185369138448,\n 0.19656669961365558,\n 0.1949845536228079,\n 0.18983715665503534,\n 0.18266938853626716,\n 0.18585890741121333,\n 0.18861255766673277,\n 0.19071304261348448,\n 0.18759401241327983,\n 0.18147207433697274,\n 0.17943393890845294,\n 0.1794327153424306,\n 0.1789776292327225,\n 0.17588171859004456,\n 0.18256161116654562,\n 0.18484921073643418,\n 0.16719472603193145,\n 0.16645689090450602,\n 0.1619121955485569,\n 0.15682363081788445,\n 0.15645268993483472,\n 0.15273080156257854,\n 0.14953095585591442,\n 0.13999577005142996,\n 0.11284003878376013,\n 0.1173729266987707,\n 0.10555822328931631,\n 0.09991732804232807,\n 0.09941060985797796,\n 0.10448384752962162,\n 0.0833640715444929,\n 0.06607803216866195,\n 0.0655552772443479,\n 0.04162728916397679,\n 0.030734693877551015,\n 0.025488215488215494,\n 0.027012195121951226,\n 0.029074074074074075,\n 0.03692876344086021,\n 0.03632478632478632,\n 0.028985507246376812,\n 0.013071895424836602,\n 0.01098901098901099,\n 0.0,\n 0.0]"
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = 0\n",
    "G2 = attack(dot, side_b)\n",
    "datas = pd.DataFrame(G2.degree())\n",
    "datas = datas.sample(frac=1)\n",
    "d = datas.shape[0]\n",
    "a=[]\n",
    "for i in range(0, 100):\n",
    "    b =int(i/100*d)\n",
    "    data1 = datas.iloc[0:b, 0]\n",
    "    G2.remove_nodes_from(data1)\n",
    "    a.append(nx.global_efficiency(G2))\n",
    "a\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "outputs": [
    {
     "data": {
      "text/plain": "[0.25220292415332296,\n 0.2480675560145663,\n 0.2444499134289015,\n 0.2383805557285003,\n 0.23562935341984073,\n 0.23234400489732823,\n 0.22892419068421935,\n 0.22490338643932575,\n 0.22027329424026412,\n 0.21532391741743315,\n 0.21229099098621063,\n 0.20749059358615665,\n 0.20422109747110367,\n 0.20108485730367776,\n 0.19837524957769795,\n 0.1939699207041949,\n 0.19135777583345132,\n 0.18586788874931065,\n 0.18024032642129328,\n 0.17515222970126137,\n 0.17285940390504423,\n 0.17011977990530358,\n 0.16581624429552433,\n 0.16209372299246577,\n 0.15668400684675654,\n 0.15278186854016948,\n 0.14722784793508448,\n 0.14266822567042994,\n 0.14014425990539744,\n 0.1341287840276018,\n 0.1294305448904243,\n 0.1251290699358392,\n 0.11557647281982224,\n 0.10566331593588234,\n 0.10047472899167037,\n 0.09586458287666329,\n 0.09166497035191742,\n 0.08128933671084354,\n 0.07111114779217856,\n 0.06622744932444116,\n 0.058540325395493906,\n 0.055575763616362926,\n 0.05506020371813221,\n 0.05429920040729074,\n 0.049944394245964474,\n 0.04750626455271723,\n 0.038839914168136175,\n 0.0366386897231846,\n 0.02098644124691174,\n 0.017700204322045287,\n 0.017725661656835593,\n 0.01807411420736453,\n 0.017831619827924975,\n 0.01400695382753608,\n 0.012212485333489329,\n 0.011620538249768313,\n 0.010778525892998053,\n 0.00998326838676577,\n 0.00942010827947793,\n 0.008887898748910853,\n 0.009162638671144089,\n 0.009291953804269063,\n 0.006370808678500985,\n 0.006000973591335037,\n 0.0058605072463768105,\n 0.0054153737274756355,\n 0.004095503659811781,\n 0.004090825519396948,\n 0.0032213157213157218,\n 0.002693497376012234,\n 0.0027455316012529947,\n 0.0022659511031604056,\n 0.0020952380952380953,\n 0.001790633608815427,\n 0.0015473032714412025,\n 0.001287001287001287,\n 0.0012114918656974732,\n 0.001142204454597373,\n 0.0012368583797155227,\n 0.0011439029970258523,\n 0.0009987515605493133,\n 0.0008403361344537816,\n 0.0006172839506172839,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0]"
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = 0\n",
    "G = attack(dot, side_b)\n",
    "datas = pd.DataFrame(G.degree())\n",
    "datas = datas.sort_values(by=1, ascending=False)\n",
    "d = datas.shape[0]\n",
    "c=[]\n",
    "for i in range(0, 100):\n",
    "    b =int(i/100*d)\n",
    "    data1 = datas.iloc[0:b, 0]\n",
    "    G.remove_nodes_from(data1)\n",
    "    c.append(nx.global_efficiency(G))\n",
    "c"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0, 0, 1, 1])\n",
    "grades_range = range(0, 100, 1)\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]  #设置字体\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False  #正常显示负号\n",
    "ax.scatter(grades_range, a, color='b', label=\"随机攻击\")\n",
    "ax.scatter(grades_range, c, color='r', label=\"蓄意攻击\")\n",
    "ax.set_xlabel('节点失效比例')\n",
    "ax.set_ylabel('网络效率')\n",
    "ax.set_title('鲁棒性分析')\n",
    "#添加图例\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "outputs": [
    {
     "data": {
      "text/plain": "                               0   1\n74       96路(永昌小区(华昌路)--北部公交枢纽站)  29\n101             127路(海源寺车场--商汇路)  28\n54        75路(西南林业大学(南院路)--眠山车场)  26\n103       129路(逸雅路口(华晨路)--北市区车场)  25\n89            115路(西苑立交桥--蓝龙潭车场)  25\n..                           ...  ..\n320           K47路(地铁机场前站--津桥学院)   0\n415  Z121路(世纪城乘车点--大都购物中心(彩云北路))   0\n172              217路(小营社区--高登村)   0\n417    Z123路(春融街地铁站C口--白龙潭淏园1号门)   0\n446  Z152路(西园路口(滇缅大道)--省建工医院生活区)   0\n\n[447 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\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>0</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>74</th>\n      <td>96路(永昌小区(华昌路)--北部公交枢纽站)</td>\n      <td>29</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>127路(海源寺车场--商汇路)</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>75路(西南林业大学(南院路)--眠山车场)</td>\n      <td>26</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>129路(逸雅路口(华晨路)--北市区车场)</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>115路(西苑立交桥--蓝龙潭车场)</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>320</th>\n      <td>K47路(地铁机场前站--津桥学院)</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>415</th>\n      <td>Z121路(世纪城乘车点--大都购物中心(彩云北路))</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>172</th>\n      <td>217路(小营社区--高登村)</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>417</th>\n      <td>Z123路(春融街地铁站C口--白龙潭淏园1号门)</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>446</th>\n      <td>Z152路(西园路口(滇缅大道)--省建工医院生活区)</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>447 rows × 2 columns</p>\n</div>"
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