part2_weibo_spreading_unfinished.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# 单条信息传播的测量与机器学习框架\n",
    "\n",
    "分析某个话题的信息扩散主要有两种方式。第一种方式是描述包括某个主题下所有信息的扩散情况，比如说“韩国部署萨德”这种政治性事件，一般在微博系统中该主题包含几十甚至成百上千条（原创）微博，既难以收集完成所有的数据，也难以对其进行量化，分析起来比较困难。另外一种方式是仅仅分析和描述一条信息完整的扩散过程，得到按照时间次序每一个转发者的信息，以及转发者相互之间的信息转发情况，进而可以构建单条信息的扩散树。比如，通过发帖者、回帖者相互之间的对话我们可以构建跟帖网络，通过分析每一个转发的信息来源，我们可以得到一个完整的信息转发网络。\n",
    "\n",
    "在本文中我们主要针对第二种分析信息扩散的方式，即描述单条信息扩散的特征。信息扩散的特征可以从多个维度进行测量，最简单的就是信息扩散的规模，而最主要的是对于信息扩散网络的描述（如广度、深度、级联率），此外还可以从时空角度来测量单条信息的信息扩散，比如信息扩散的时间分布特征（速度、爆发、持续性）、空间分布特征。另外，可以结合信息扩散者的社会网络信息来描述信息扩散的特征，比如计算每个转发者的信息扩散阈值等。\n",
    "\n",
    "除了对单条信息的诸多方面进行描述，我们也可以分析影响前面信息传播的各种因素。这种分析既可以是采用比较简单的方式，如观察信息扩散的规模与某项统计特征事件是否存在相关关系。也可以采用比较复杂的方式，如我们构建一个机器学习的框架，通过数据预处理、特征工程、特征选择、构建分类器等诸多步骤来定量化分析不同因素对于单条信息传播的影响。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 数据说明\n",
    "\n",
    "如果要对微博传播进行量化，首先要做的一项工作就是获得大规模有代表性的实证数据。在本文中，我们使用卧龙大数据发起的一个微博传播竞赛的数据，详见[微博传播竞赛](http://www.pkbigdata.com/common/cmpt/%E5%BE%AE%E5%8D%9A%E7%83%AD%E5%BA%A6%E9%A2%84%E6%B5%8B_%E7%AB%9E%E8%B5%9B%E4%BF%A1%E6%81%AF.html)\n",
    "。在该数据集中，他们抓取了大概约3万条“源微博”数据，这些“源微博”被转发共计超过1784万次，涉及到约800万位用户，用户之间的关注关系超过7亿条。 \n",
    "可供训练学习的数据包括：一批源微博的转发数据，包括被转发的用户，产生转发行为的用户，微博的内容、发布时间；用户与用户之间的关注关系。 测试集的数据：一批刚刚开始传播的微博转发数据及每条微博（包括转发微博）的内容。参赛者需要学习训练集，然后预测测试集中的微博的传播规模和深度。\n",
    "\n",
    "近些年，一些研究表明，一条微博发出以后，只需要观察其在之后一小段时间内的转发情况，它的传播规模便可以被预测。但是不同类型的微博会有不同的传播方式，比如明星晒一张生活状态就能得到众多粉丝的热捧，具有较大的传播广度，但是往往在传播深度上稍显不足；相比之下，一些被广泛讨论的新闻类微博往往具有较深的传播深度。也有统计结果显示，一些谣言往往会得到大规模的传播，辟谣类的消息反而得不到广泛关注。不仅如此，我们在热门微博中能看到不少正能量的信息，同时也能看到一些话题被持正反两种不同意见的人掀起讨论热潮。\n",
    "\n",
    "简而言之，微博初期的传播速度、用户关系、信息类型、内容情感等特征都是影响微博传播规模和深度的重要影响因素。 为预测微博的传播规模，我们收集了大约1-3万条微博及其它们的转发微博，希望参赛者能够结合微博用户的关注关系、微博的内容类型和情感分析以及初期的传播模式，来预测微博的传播规模(传播规模定义：源微博一共有多少人转发)和传播深度（传播深度定义：源微博到其他转发微博的最长距离）。参赛者需要建立模型来模拟微博在社交网络中的传播过程；然后将模型应用于“测试集的微博”，以预测它们转发量以及转发深度的增长。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据下载地址\n",
    "初赛：[https://pan.baidu.com/s/1dELaBJz](https://pan.baidu.com/s/1dELaBJz)\n",
    "提取密码：samn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 所有代码的运行环境\n",
    "需要依赖：Python2.7，pandas，xgboost，graphlab，treelib，jieba，gensim，scikit-learn，networkx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 构建数据处理的初始化环境"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#导入各种要使用的模块\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import graphlab as gl\n",
    "from treelib import Tree\n",
    "import cPickle as pickle\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-09-22 16:40:39,262 [INFO] graphlab.cython.cy_server, 176: GraphLab Create v1.9 started. Logging: /tmp/graphlab_server_1506069638.log\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This non-commercial license of GraphLab Create for academic use is assigned to xiaokeeie@gmail.com and will expire on October 16, 2017.\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "#构建画图环境\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 整理数据格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#因为pandas读入这个数据有问题，所以得先使用graphlab处理一下该数据\n",
    "#采用graphlab框架读入trainRepost.txt数据\n",
    "#trainRepost=gl.SFrame.read_csv('trainRepost.txt',sep = '\\x01',header =False)\n",
    "#trainRepost.save('trainRepost_origin.csv', format='csv')  #可提供trainRepost_origin.csv数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#采用graphlab框架读入并保存WeiboProfile_train数据\n",
    "#Weibo_train=gl.SFrame.read_csv('WeiboProfile.train',sep = '\\x01',header =False)\n",
    "#Weibo_train.save('WeiboProfile_train.csv', format='csv') #可提供WeiboProfile_train.csv数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 关注关系特征的构建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 关注关系数据格式的说明\n",
    "相关用户的关注关系，文件名：userRelations.zip \n",
    "\n",
    "字段格式为：用户id1 id1关注的用户id列表     \n",
    "例如：id1 id2\\001id3\\001id4 \n",
    "\n",
    "样例：小张关注了小王、小李和小赵，小王关注了小李和小赵，则数据中表示为：     \n",
    "小张id 小王id\\001小李id\\001小赵id     \n",
    "小王id 小李id\\001小赵id \n",
    "\n",
    "注意：每行表示一个用户关注的微博用户列表，id1和id2之间空格分隔，用以区分用户和关注列表。分隔符为\\001，若用python处理，可以试试\\x001"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 关注关系中需要构建的结构特征\n",
    "\n",
    "### 3.2.1 发微博节点在关注关系中的结构特征\n",
    "\n",
    "- 发微博节点的出度：out_degree1\n",
    "- 发微博节点的入度：in_degree1\n",
    "- 发微博节点在无向网络中的度：all_degree1\n",
    "- 发微博节点的双向朋友数量：bi_degree1\n",
    "- 发微博节点的pagerank值：pagerank1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.2 发微博节点和转发微博所有节点在关注关系中的结构特征\n",
    "\n",
    "发微博节点和所有60分钟内转发微博节点的\n",
    "\n",
    "- 出度之和：sum_out_degree1\n",
    "- 入度之和：sum_in_degree1\n",
    "- 在无向网络中的度之和：sum_all_degree1\n",
    "- 双向朋友数量之和：sum_bi_degree1\n",
    "- pagerank值的之和：sum_pagerank1\n",
    "\n",
    "假设节点数量为$n$,连边的数量为$m$,则网络密度的计算公式为$density=\\frac{2m}{n(n-1)}$。\n",
    "\n",
    "发微博节点和所有60分钟内转发微博节点的：\n",
    "\n",
    "- 出度链接密度：n1_out_density\n",
    "- 入度链接密度：n1_in_density\n",
    "- 无向网络中的链接密度：n1_all_density\n",
    "- 双向朋友数量：n1_bi_density\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 关注关系数据中结构特征构建的详细过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
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      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[int,int]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n",
      "+---------+---------+\n",
      "|   uid1  |   uid2  |\n",
      "+---------+---------+\n",
      "| 4534517 | 6571120 |\n",
      "| 4534517 | 2453117 |\n",
      "| 4534517 | 6105970 |\n",
      "| 4534517 | 2666130 |\n",
      "| 4534517 | 8207859 |\n",
      "| 4534517 | 1843342 |\n",
      "| 4534517 | 6342579 |\n",
      "| 4534517 | 7610017 |\n",
      "| 4534517 | 8237366 |\n",
      "| 4534517 | 8236591 |\n",
      "+---------+---------+\n",
      "[10 rows x 2 columns]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## 导入微博关注关系数据集\n",
    "##guanzhu_links=gl.SFrame.read_csv('relations.csv')                         #导入全部用户之间的链接关系，可提供该数据\n",
    "#guanzhu_links=gl.SFrame.read_csv('user_relations.csv', nrows=10000)        #导入少量用户之间的链接关系\n",
    "guanzhu_links=guanzhu_links.dropna()                                        #删除链接关系中的缺失值\n",
    "print guanzhu_links.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'num_edges': 700434403, 'num_vertices': 7977942}\n"
     ]
    }
   ],
   "source": [
    "## 使用SGraph来生成微博用户之间的关系网络G1\n",
    "G1=gl.SGraph().add_edges(guanzhu_links, src_field = 'uid1', dst_field = 'uid2')\n",
    "print G1.summary()                                            #做一个简单的信息汇总"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#G1.save('G1')                                              #可以直接保存该网络的数据，可提供该数据G1\n",
    "G1= gl.load_sgraph('G1')                                    #也可以将该网络读入到内存空间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
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       "    </tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">91283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">91415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">91420</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 1 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+-------+\n",
       "|   id  |\n",
       "+-------+\n",
       "| 87442 |\n",
       "| 87619 |\n",
       "| 88814 |\n",
       "| 89844 |\n",
       "| 90916 |\n",
       "| 91044 |\n",
       "| 91220 |\n",
       "| 91283 |\n",
       "| 91415 |\n",
       "| 91420 |\n",
       "+-------+\n",
       "[10 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_G1 = G1.get_vertices()                                  #获取网络中所有节点的名称\n",
    "users_G1.rename({'__id':'id'})                                #将名称重新命名\n",
    "users_G1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#计算每一个节点的出度和入度\n",
    "user_out_friends = guanzhu_links.groupby('uid1', {'out_friends1' : gl.aggregate.CONCAT('uid2')}) #out_friends\n",
    "user_out_friends.rename({'uid1': 'id'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">in_friends1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8342756</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[4982260]</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8279084</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[8049434, 3968430,<br>8280045, 7563538, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7226615</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[5331852, 4982260]</td>\n",
       "    </tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[4823800]</td>\n",
       "    </tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[7456076]</td>\n",
       "    </tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5453402</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[4823800]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7894256</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[5927121]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7591144</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[2199577]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5570026</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[5927121]</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[6041 rows x 2 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\tin_friends1\tlist\n",
       "\n",
       "Rows: 6041\n",
       "\n",
       "Data:\n",
       "+---------+-------------------------------+\n",
       "|    id   |          in_friends1          |\n",
       "+---------+-------------------------------+\n",
       "| 8342756 |           [4982260]           |\n",
       "| 3097696 |           [1686731]           |\n",
       "| 8279084 | [8049434, 3968430, 8280045... |\n",
       "| 7226615 |       [5331852, 4982260]      |\n",
       "| 7269716 |           [4823800]           |\n",
       "| 6482075 |           [7456076]           |\n",
       "| 5453402 |           [4823800]           |\n",
       "| 7894256 |           [5927121]           |\n",
       "| 7591144 |           [2199577]           |\n",
       "| 5570026 |           [5927121]           |\n",
       "+---------+-------------------------------+\n",
       "[6041 rows x 2 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_in_friends = guanzhu_links.groupby('uid2', {'in_friends1' : gl.aggregate.CONCAT('uid1')})   #in_friends\n",
    "user_in_friends.rename({'uid2' : 'id'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
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       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">in_friends1</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">out_friends1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8342756</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[4982260]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3097696</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[1686731]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8279084</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[8049434, 3968430,<br>8280045, 7563538, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7226615</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[5331852, 4982260]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "    </tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7269716</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[7456076]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[4823800]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
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       "    <tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7591144</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[2199577]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5570026</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[5927121]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 3 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\tin_friends1\tlist\n",
       "\tout_friends1\tlist\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+---------+-------------------------------+--------------+\n",
       "|    id   |          in_friends1          | out_friends1 |\n",
       "+---------+-------------------------------+--------------+\n",
       "| 8342756 |           [4982260]           |      []      |\n",
       "| 3097696 |           [1686731]           |      []      |\n",
       "| 8279084 | [8049434, 3968430, 8280045... |      []      |\n",
       "| 7226615 |       [5331852, 4982260]      |      []      |\n",
       "| 7269716 |           [4823800]           |      []      |\n",
       "| 6482075 |           [7456076]           |      []      |\n",
       "| 5453402 |           [4823800]           |      []      |\n",
       "| 7894256 |           [5927121]           |      []      |\n",
       "| 7591144 |           [2199577]           |      []      |\n",
       "| 5570026 |           [5927121]           |      []      |\n",
       "+---------+-------------------------------+--------------+\n",
       "[10 rows x 3 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_G1 = users_G1.join(user_in_friends, on = 'id', how = 'outer')   \n",
    "users_G1 = users_G1.join(user_out_friends, on = 'id', how = 'outer')\n",
    "\n",
    "#在下面使用空列表来代替缺失值\n",
    "users_G1 = users_G1.fillna('in_friends1', [ ])\n",
    "users_G1 = users_G1.fillna('out_friends1', [ ])\n",
    "users_G1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
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       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">in_friends1</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">out_friends1</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">out_degree1</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">in_degree1</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">all_friends1</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">all_degree1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8342756</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3097696</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[1686731]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[1686731.0]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8279084</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[8049434, 3968430,<br>8280045, 7563538, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[7563538.0, 2432261.0,<br>6712486.0, 6534809.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7226615</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[5331852, 4982260]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
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       "<table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bi_friends1</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bi_degree1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
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       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
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       "    </tr>\n",
       "</table>\n",
       "[10 rows x 9 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\tin_friends1\tlist\n",
       "\tout_friends1\tlist\n",
       "\tout_degree1\tint\n",
       "\tin_degree1\tint\n",
       "\tall_friends1\tarray\n",
       "\tall_degree1\tint\n",
       "\tbi_friends1\tlist\n",
       "\tbi_degree1\tint\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+---------+-------------------------------+--------------+-------------+------------+\n",
       "|    id   |          in_friends1          | out_friends1 | out_degree1 | in_degree1 |\n",
       "+---------+-------------------------------+--------------+-------------+------------+\n",
       "| 8342756 |           [4982260]           |      []      |      0      |     1      |\n",
       "| 3097696 |           [1686731]           |      []      |      0      |     1      |\n",
       "| 8279084 | [8049434, 3968430, 8280045... |      []      |      0      |     7      |\n",
       "| 7226615 |       [5331852, 4982260]      |      []      |      0      |     2      |\n",
       "| 7269716 |           [4823800]           |      []      |      0      |     1      |\n",
       "| 6482075 |           [7456076]           |      []      |      0      |     1      |\n",
       "| 5453402 |           [4823800]           |      []      |      0      |     1      |\n",
       "| 7894256 |           [5927121]           |      []      |      0      |     1      |\n",
       "| 7591144 |           [2199577]           |      []      |      0      |     1      |\n",
       "| 5570026 |           [5927121]           |      []      |      0      |     1      |\n",
       "+---------+-------------------------------+--------------+-------------+------------+\n",
       "+-------------------------------+-------------+-------------+------------+\n",
       "|          all_friends1         | all_degree1 | bi_friends1 | bi_degree1 |\n",
       "+-------------------------------+-------------+-------------+------------+\n",
       "|          [4982260.0]          |      1      |      []     |     0      |\n",
       "|          [1686731.0]          |      1      |      []     |     0      |\n",
       "| [7563538.0, 2432261.0, 671... |      7      |      []     |     0      |\n",
       "|     [5331852.0, 4982260.0]    |      2      |      []     |     0      |\n",
       "|          [4823800.0]          |      1      |      []     |     0      |\n",
       "|          [7456076.0]          |      1      |      []     |     0      |\n",
       "|          [4823800.0]          |      1      |      []     |     0      |\n",
       "|          [5927121.0]          |      1      |      []     |     0      |\n",
       "|          [2199577.0]          |      1      |      []     |     0      |\n",
       "|          [5927121.0]          |      1      |      []     |     0      |\n",
       "+-------------------------------+-------------+-------------+------------+\n",
       "[10 rows x 9 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#out_degree - number of users each vertex is following \n",
    "users_G1['out_degree1'] = users_G1[\"out_friends1\"].apply(lambda l: len(l) ) \n",
    "\n",
    "#in_degree - number of users following each vertex \n",
    "users_G1['in_degree1'] = users_G1[\"in_friends1\"].apply(lambda l: len(l) ) \n",
    "\n",
    "#all_degree - number of uniuqe users that following or are followed by each user \n",
    "users_G1['all_friends1'] = users_G1[['in_friends1', 'out_friends1']].apply(lambda r: list(set(r['in_friends1']) | set(r['out_friends1']))) \n",
    "users_G1['all_degree1'] = users_G1[\"all_friends1\"].apply(lambda l: len(l) ) \n",
    "\n",
    "#bi_degree - number of uniuqe users that are both following and followed by each user \n",
    "users_G1['bi_friends1'] = users_G1[['in_friends1', 'out_friends1']].apply(lambda r: list(set(r['in_friends1']) & set(r['out_friends1']))) \n",
    "users_G1['bi_degree1'] = users_G1[\"bi_friends1\"].apply(lambda l: len(l) ) \n",
    "\n",
    "users_G1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "</table>\n",
       "[10 rows x 6 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\tout_degree1\tint\n",
       "\tin_degree1\tint\n",
       "\tall_degree1\tint\n",
       "\tbi_degree1\tint\n",
       "\tpagerank2\tfloat\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+---------+-------------+------------+-------------+------------+----------------+\n",
       "|    id   | out_degree1 | in_degree1 | all_degree1 | bi_degree1 |   pagerank2    |\n",
       "+---------+-------------+------------+-------------+------------+----------------+\n",
       "| 8342756 |      0      |     1      |      1      |     0      | 0.150745614035 |\n",
       "| 3097696 |      0      |     1      |      1      |     0      |      0.15      |\n",
       "| 8279084 |      0      |     7      |      7      |     0      |      0.15      |\n",
       "| 7226615 |      0      |     2      |      2      |     0      | 0.151613924051 |\n",
       "| 7269716 |      0      |     1      |      1      |     0      | 0.155658507161 |\n",
       "| 6482075 |      0      |     1      |      1      |     0      | 0.153384779453 |\n",
       "| 5453402 |      0      |     1      |      1      |     0      | 0.150689189189 |\n",
       "| 7894256 |      0      |     1      |      1      |     0      | 0.150435153584 |\n",
       "| 7591144 |      0      |     1      |      1      |     0      | 0.15131443299  |\n",
       "| 5570026 |      0      |     1      |      1      |     0      | 0.15108974359  |\n",
       "+---------+-------------+------------+-------------+------------+----------------+\n",
       "[10 rows x 6 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del users_G1['in_friends1']\n",
    "del users_G1['out_friends1']\n",
    "del users_G1['all_friends1']\n",
    "del users_G1['bi_friends1']\n",
    "users_G1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<pre>Counting out degree</pre>"
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       "Counting out degree"
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      "text/html": [
       "<pre>Done counting out degree</pre>"
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       "Done counting out degree"
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     "data": {
      "text/html": [
       "<pre>+-----------+-----------------------+</pre>"
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       "+-----------+-----------------------+"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<pre>| Iteration | L1 change in pagerank |</pre>"
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      "text/plain": [
       "| Iteration | L1 change in pagerank |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<pre>+-----------+-----------------------+</pre>"
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    {
     "data": {
      "text/html": [
       "<pre>| 1         | 5134.85               |</pre>"
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      "text/html": [
       "<pre>| 2         | 85.9775               |</pre>"
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       "    </tr>\n",
       "</table>\n",
       "[10 rows x 6 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\ttweet_user\tint\n",
       "\tout_degree1\tint\n",
       "\tin_degree1\tint\n",
       "\tall_degree1\tint\n",
       "\tbi_degree1\tint\n",
       "\tpagerank1\tfloat\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+------------+-------------+------------+-------------+------------+\n",
       "| tweet_user | out_degree1 | in_degree1 | all_degree1 | bi_degree1 |\n",
       "+------------+-------------+------------+-------------+------------+\n",
       "|  8342756   |      0      |     1      |      1      |     0      |\n",
       "|  3097696   |      0      |     1      |      1      |     0      |\n",
       "|  8279084   |      0      |     7      |      7      |     0      |\n",
       "|  7226615   |      0      |     2      |      2      |     0      |\n",
       "|  7269716   |      0      |     1      |      1      |     0      |\n",
       "|  6482075   |      0      |     1      |      1      |     0      |\n",
       "|  5453402   |      0      |     1      |      1      |     0      |\n",
       "|  7894256   |      0      |     1      |      1      |     0      |\n",
       "|  7591144   |      0      |     1      |      1      |     0      |\n",
       "|  5570026   |      0      |     1      |      1      |     0      |\n",
       "+------------+-------------+------------+-------------+------------+\n",
       "+----------------+\n",
       "|   pagerank1    |\n",
       "+----------------+\n",
       "| 0.150745614035 |\n",
       "|      0.15      |\n",
       "|      0.15      |\n",
       "| 0.151613924051 |\n",
       "| 0.155658507161 |\n",
       "| 0.153384779453 |\n",
       "| 0.150689189189 |\n",
       "| 0.150435153584 |\n",
       "| 0.15131443299  |\n",
       "| 0.15108974359  |\n",
       "+----------------+\n",
       "[10 rows x 6 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 计算出每一个节点的pagerank值\n",
    "pr1 = gl.pagerank.create(G1)\n",
    "users_G1['pagerank1'] = pr1['graph'].vertices['pagerank'] #add the new pagerank field to the original graph g2\n",
    "users_G1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/users_G1.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/users_G1.csv"
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      "text/html": [
       "<pre>Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/users_G1.csv</pre>"
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       "Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/users_G1.csv"
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      "text/html": [
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       "Parsing completed. Parsed 7977942 lines in 4.24228 secs."
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     },
     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[int,float,int,int,int,int]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">91220</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.154487</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">91283</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.152531</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">91415</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.153186</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">91420</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.153889</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 6 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\ttweet_user\tint\n",
       "\tpagerank1\tfloat\n",
       "\tout_degree1\tint\n",
       "\tin_degree1\tint\n",
       "\tall_degree1\tint\n",
       "\tbi_degree1\tint\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+------------+-----------+-------------+------------+-------------+------------+\n",
       "| tweet_user | pagerank1 | out_degree1 | in_degree1 | all_degree1 | bi_degree1 |\n",
       "+------------+-----------+-------------+------------+-------------+------------+\n",
       "|   87442    |  0.150963 |      0      |     1      |      1      |     0      |\n",
       "|   87619    |  0.152167 |      0      |     1      |      1      |     0      |\n",
       "|   88814    |  0.151342 |      0      |     1      |      1      |     0      |\n",
       "|   89844    |  0.151342 |      0      |     1      |      1      |     0      |\n",
       "|   90916    |  0.153678 |      0      |     1      |      1      |     0      |\n",
       "|   91044    |  0.151316 |      0      |     1      |      1      |     0      |\n",
       "|   91220    |  0.154487 |      0      |     2      |      2      |     0      |\n",
       "|   91283    |  0.152531 |      0      |     1      |      1      |     0      |\n",
       "|   91415    |  0.153186 |      0      |     1      |      1      |     0      |\n",
       "|   91420    |  0.153889 |      0      |     2      |      2      |     0      |\n",
       "+------------+-----------+-------------+------------+-------------+------------+\n",
       "[10 rows x 6 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将其保存为csv文件以便今后使用\n",
    "#users_G1.rename({'id': 'tweet_user'})                      #更改为tweet_user以便进行数据连接\n",
    "#users_G1.save('users_G1.csv', format='csv')                #可提供该数据users_G1，通过服务器计算的，所以列名字稍有差异\n",
    "\n",
    "\n",
    "#使用graphlab重新将数据载入到内存空间中\n",
    "users_G1=gl.SFrame.read_csv('users_G1.csv')\n",
    "users_G1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eBvYCX2+SDCuqagKgqvYCZzfNVwEvtXXf08RWAbvb4rub2LQ+VfU28HqSM482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\nkiRJkiRp5OygkCRJkiRJI/df5zVMiGjCXx0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11534e090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = gcf() \n",
    "fig.set_size_inches(18, 12)\n",
    "\n",
    "subplot(2,2,1)\n",
    "out_degree=users_G1['out_degree1']\n",
    "hist(out_degree,range(1,500))\n",
    "xlabel('out_degree')\n",
    "\n",
    "subplot(2,2,2)\n",
    "in_degree=users_G1['in_degree1']\n",
    "hist(in_degree,range(1,100))\n",
    "xlabel('in_degree')\n",
    "\n",
    "subplot(2,2,3)\n",
    "all_degree=users_G1['all_degree1']\n",
    "hist(all_degree,range(1,500))\n",
    "xlabel('all_degree')\n",
    "\n",
    "subplot(2,2,4)\n",
    "bi_degree=users_G1['bi_degree1']\n",
    "hist(bi_degree,range(1,100))\n",
    "xlabel('bi_degree')\n",
    "\n",
    "savefig(\"network1_degree.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11ce73ad0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6ssuSV72qSZUBrcgKMwAzzoibAg89NKvOPTc544zkrLOSnh4bA4Ex0cMMwIw0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\nAAANCMwAANCAwAwAAA38f9GzKjF64Z+lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ec90a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = gcf() \n",
    "fig.set_size_inches(12, 9)\n",
    "in_degree=users_G1['in_degree1']\n",
    "in_degree_hist=hist(in_degree,range(1,100))\n",
    "xbin=list(in_degree_hist[1])\n",
    "del xbin[-1]\n",
    "ybin=list(in_degree_hist[0])\n",
    "loglog(xbin,ybin,'ro-')\n",
    "xlabel('in_degree')\n",
    "savefig(\"in_degree.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 微博转发结构和时间特征的构建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 原始数据格式的说明\n",
    "用户微博的转发数据，文件名为trainRepost.txt \n",
    "\n",
    "字段格式为：微博id\\001被转发用户id\\001转发用户id\\001转发时间与源微博发表时间的间隔\\001微博内容  \n",
    "样例：小赵在早上7点15分10秒发了一条微博“早上好”，假设记id为weibo1；  \n",
    "然后按时间先后，过了10s，小王转发了此微博；  \n",
    "此后又过了20s，小李也转发了；  \n",
    "再过去30s，小张从小王那里转发了此微博。  \n",
    "\n",
    "微博上的记录如下：     \n",
    "小赵：早上好     \n",
    "小王：早//@小赵：早上好     \n",
    "小李：早安//@小赵：早上好     \n",
    "小张：转发微博//@小王：早//@小赵：早上好       \n",
    "那么微博转发数据中的记录如下：     \n",
    "weibo1\\001小赵id\\001小王id\\00110\\001早    \n",
    "weibo1\\001小赵id\\001小李id\\00130\\001早安     \n",
    "weibo1\\001小王id\\001小张id\\00160\\001    \n",
    "说明：每行用“回车符”分隔，一行内各字段用“\\001”分隔。若转发内容为“转发微博”，我们则省略显示此字段。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 转发关系中需要构建的传播特征\n",
    "\n",
    "### 4.2.1 转发网络中的结构特征\n",
    "- 节点的出度：out_degree2\n",
    "- 节点的入度：in_degree2\n",
    "- 节点在无向网络中的度：all_degree2\n",
    "- 节点的双向朋友数量：bi_degree2\n",
    "- 节点的pagerank值：pagerank2\n",
    "\n",
    "### 4.2.2 转发网络中的时间特征\n",
    "- 1-60分钟时间内转发的规模：WeiboID,scaleT1,scaleT1…,scaleT60\n",
    "- 1-60分钟时间内转发的深度：WeiboID,depthT1,depthT1…,depthT60\n",
    "- 60分钟内的变异系数：差分时间序列的标准差/差分时间序列的均值\n",
    "- 60分钟内的峰值比率：差分时间序列的最大转发量/总转发量\n",
    "- 75分钟起每个15分钟一次的总转发规模：WeiboID (Time Unit: Minutes),scaleT75,scaleT90…,scaleT4380\n",
    "- 75分钟起每个15分钟一次的总转发规模：WeiboID (Time Unit: Minutes),depthT75,depthT90…,depthT4380 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 对数据进行读取、设置列名、排序等预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3.1 读取文件trainRepost，设置列名，按照微博号排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weibo_id</th>\n",
       "      <th>tweet_user</th>\n",
       "      <th>retweet_user</th>\n",
       "      <th>spread_time</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3903741306239294</td>\n",
       "      <td>2848188</td>\n",
       "      <td>8381213</td>\n",
       "      <td>58674</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3972184907435086</td>\n",
       "      <td>5368578</td>\n",
       "      <td>8381203</td>\n",
       "      <td>53713</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3903069714925188</td>\n",
       "      <td>6854938</td>\n",
       "      <td>8381203</td>\n",
       "      <td>7828688</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3934248572099610</td>\n",
       "      <td>4130595</td>\n",
       "      <td>8381203</td>\n",
       "      <td>299103</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3945441579517170</td>\n",
       "      <td>7853791</td>\n",
       "      <td>8381203</td>\n",
       "      <td>11929</td>\n",
       "      <td>@空仕安forever @余瑞廉2013 @吴成军1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3920377703250479</td>\n",
       "      <td>2699651</td>\n",
       "      <td>8381203</td>\n",
       "      <td>3820213</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3934928376650489</td>\n",
       "      <td>6666106</td>\n",
       "      <td>8381203</td>\n",
       "      <td>137272</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3929327043353030</td>\n",
       "      <td>1639686</td>\n",
       "      <td>8381203</td>\n",
       "      <td>1645635</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3811632822151194</td>\n",
       "      <td>5860754</td>\n",
       "      <td>8381203</td>\n",
       "      <td>29320554</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3907606521769298</td>\n",
       "      <td>6560549</td>\n",
       "      <td>8381203</td>\n",
       "      <td>8714507</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           weibo_id  tweet_user  retweet_user  spread_time  \\\n",
       "0  3903741306239294     2848188       8381213        58674   \n",
       "1  3972184907435086     5368578       8381203        53713   \n",
       "2  3903069714925188     6854938       8381203      7828688   \n",
       "3  3934248572099610     4130595       8381203       299103   \n",
       "4  3945441579517170     7853791       8381203        11929   \n",
       "5  3920377703250479     2699651       8381203      3820213   \n",
       "6  3934928376650489     6666106       8381203       137272   \n",
       "7  3929327043353030     1639686       8381203      1645635   \n",
       "8  3811632822151194     5860754       8381203     29320554   \n",
       "9  3907606521769298     6560549       8381203      8714507   \n",
       "\n",
       "                         content  \n",
       "0                            NaN  \n",
       "1                            NaN  \n",
       "2                            NaN  \n",
       "3                            NaN  \n",
       "4  @空仕安forever @余瑞廉2013 @吴成军1982  \n",
       "5                            NaN  \n",
       "6                            NaN  \n",
       "7                            NaN  \n",
       "8                            NaN  \n",
       "9                            NaN  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#为每一列起一个名字，方便今后的操作\n",
    "column_names=['weibo_id','tweet_user','retweet_user','spread_time','content']\n",
    "\n",
    "#采用pandas框架读入数据\n",
    "trainRepost=pd.read_csv('trainRepost_origin.csv',header=0,names=column_names)\n",
    "\n",
    "#未排序前的数据\n",
    "trainRepost.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15975388"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看一下数据长度，看看读取过程是否有问题，正确长度应该为15975388\n",
    "len(trainRepost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weibo_id</th>\n",
       "      <th>tweet_user</th>\n",
       "      <th>retweet_user</th>\n",
       "      <th>spread_time</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7329356</th>\n",
       "      <td>3793991852126437</td>\n",
       "      <td>8216045</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>13823173</th>\n",
       "      <td>3793991852126437</td>\n",
       "      <td>8216045</td>\n",
       "      <td>3063794</td>\n",
       "      <td>210</td>\n",
       "      <td>很好看，@赵雅芝 新年快乐@陳法蓉 新年快乐，2015祝你幸福，尽快</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5502957</th>\n",
       "      <td>3793991852126437</td>\n",
       "      <td>8216045</td>\n",
       "      <td>4785212</td>\n",
       "      <td>450</td>\n",
       "      <td>新年快樂[鼓掌]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13728515</th>\n",
       "      <td>3793991852126437</td>\n",
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       "      <td>8216045</td>\n",
       "      <td>8297102</td>\n",
       "      <td>656</td>\n",
       "      <td>[爱你][爱你]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331786</th>\n",
       "      <td>3793991852126437</td>\n",
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       "      <td>689</td>\n",
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       "      <td>8216045</td>\n",
       "      <td>6606949</td>\n",
       "      <td>824</td>\n",
       "      <td>要不要這麼好[花心][花心]2015。幸福的開始</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9027973</th>\n",
       "      <td>3793991852126437</td>\n",
       "      <td>8216045</td>\n",
       "      <td>3207988</td>\n",
       "      <td>944</td>\n",
       "      <td>芝姐美美哒😍</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  weibo_id  tweet_user  retweet_user  spread_time  \\\n",
       "7329356   3793991852126437     8216045       3558011          114   \n",
       "13823173  3793991852126437     8216045       3063794          210   \n",
       "5502957   3793991852126437     8216045       4785212          450   \n",
       "13728515  3793991852126437     8216045       3546267          587   \n",
       "9638528   3793991852126437     8216045       8297102          656   \n",
       "331786    3793991852126437     8216045       6726058          689   \n",
       "8715405   3793991852126437     8216045       4748782          693   \n",
       "11690426  3793991852126437     8216045       5803291          785   \n",
       "1971640   3793991852126437     8216045       6606949          824   \n",
       "9027973   3793991852126437     8216045       3207988          944   \n",
       "\n",
       "                                     content  \n",
       "7329356                                  NaN  \n",
       "13823173  很好看，@赵雅芝 新年快乐@陳法蓉 新年快乐，2015祝你幸福，尽快  \n",
       "5502957                             新年快樂[鼓掌]  \n",
       "13728515                                 NaN  \n",
       "9638528                             [爱你][爱你]  \n",
       "331786                                   NaN  \n",
       "8715405                                  NaN  \n",
       "11690426                                 NaN  \n",
       "1971640             要不要這麼好[花心][花心]2015。幸福的開始  \n",
       "9027973                              芝姐美美哒😍  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按照微博webo_id,转发时间spread_time, 被转发用户tweet_user，转发用户retweet_user排序，升序排列\n",
    "#排序后对于创建树形结构是非常有利的\n",
    "trainRepost.sort_values(by=['weibo_id','spread_time','tweet_user','retweet_user'],ascending=[True,True,True,True])\n",
    "\n",
    "#排序后的数据\n",
    "trainRepost.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#将其保存为csv文件以便今后使用\n",
    "#trainRepost.to_csv('trainRepost_sort.csv',index=False) #可提供trainRepost_sort.csv数据\n",
    "\n",
    "#重新将数据载入到内存空间中\n",
    "trainRepost=pd.read_csv('trainRepost_sort.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1190b2810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对传播时间作一个简要的分析\n",
    "fig = gcf() \n",
    "fig.set_size_inches(15, 9)\n",
    "spread_time=trainRepost['spread_time']/60.0\n",
    "hist(spread_time,np.arange(0,1200,15))\n",
    "xlabel('Minute')\n",
    "savefig(\"spread_result.pdf\")\n",
    "#由上图可以看出，大量的转发是在前100分钟内完成的，因此前一个小时的转发信息是有可能预测后面的转发情况。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 转发关系数据中结构特征构建的详细过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/zhuanfa_links.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/zhuanfa_links.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 100 lines in 0.741369 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 100 lines in 0.741369 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Read 2378481 lines. Lines per second: 1.88315e+06</pre>"
      ],
      "text/plain": [
       "Read 2378481 lines. Lines per second: 1.88315e+06"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/zhuanfa_links.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/zhuanfa_links.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 15975387 lines in 4.33957 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 15975387 lines in 4.33957 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[int,int,int]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">src</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">dst</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">spread_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3558011</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8216045</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8216045</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8216045</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">587</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8297102</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">689</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">693</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5803291</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6606949</td>\n",
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       "    </tr>\n",
       "</table>\n",
       "[10 rows x 3 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tsrc\tint\n",
       "\tdst\tint\n",
       "\tspread_time\tint\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+---------+---------+-------------+\n",
       "|   src   |   dst   | spread_time |\n",
       "+---------+---------+-------------+\n",
       "| 3558011 | 8216045 |     114     |\n",
       "| 3063794 | 8216045 |     210     |\n",
       "| 4785212 | 8216045 |     450     |\n",
       "| 3546267 | 8216045 |     587     |\n",
       "| 8297102 | 8216045 |     656     |\n",
       "| 6726058 | 8216045 |     689     |\n",
       "| 4748782 | 8216045 |     693     |\n",
       "| 5803291 | 8216045 |     785     |\n",
       "| 6606949 | 8216045 |     824     |\n",
       "| 3207988 | 8216045 |     944     |\n",
       "+---------+---------+-------------+\n",
       "[10 rows x 3 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#zhuanfa_links=trainRepost[['retweet_user','tweet_user','spread_time']]\n",
    "#zhuanfa_links=zhuanfa_links.dropna()                                               #删除链接关系中的缺失值\n",
    "#zhuanfa_links.columns = ['src', 'dst','spread_time']\n",
    "\n",
    "#将其保存为csv文件以便今后使用\n",
    "#zhuanfa_links.to_csv('zhuanfa_links.csv',index=False)                             #可提供zhuanfa_links.csv数据\n",
    "\n",
    "#使用graphlab重新将数据载入到内存空间中\n",
    "zhuanfa_links=gl.SFrame.read_csv('zhuanfa_links.csv')\n",
    "zhuanfa_links.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'num_edges': 15975387, 'num_vertices': 7445377}\n"
     ]
    }
   ],
   "source": [
    "## 使用SGraph来生成微博用户之间的关系网络G2\n",
    "G2=gl.SGraph().add_edges(zhuanfa_links, src_field = 'src', dst_field = 'dst')\n",
    "print G2.summary()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#G2.save('G2')                                                #可以直接保存该网络的数据，可提供转发网络G2的数据\n",
    "G2= gl.load_sgraph('G2')                                      #将该网络读入到内存空间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "</table>\n",
       "[10 rows x 1 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+----+\n",
       "| id |\n",
       "+----+\n",
       "| 5  |\n",
       "| 7  |\n",
       "| 8  |\n",
       "| 10 |\n",
       "| 27 |\n",
       "| 29 |\n",
       "| 33 |\n",
       "| 47 |\n",
       "| 52 |\n",
       "| 73 |\n",
       "+----+\n",
       "[10 rows x 1 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_G2 = G2.get_vertices()                                  #获取网络中所有节点的名称\n",
    "users_G2.rename({'__id':'id'})                                #将名称重新命名\n",
    "users_G2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
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       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">out_friends2</th>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1261821</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[8202919, 8202919]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">504649</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[7377993, 5511390]</td>\n",
       "    </tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7392217</td>\n",
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       "    </tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1185276</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[7899465]</td>\n",
       "    </tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[4516159]</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 2 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\tout_friends2\tlist\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+---------+--------------------+\n",
       "|    id   |    out_friends2    |\n",
       "+---------+--------------------+\n",
       "| 4865614 |     [7659055]      |\n",
       "| 6120761 |     [5933434]      |\n",
       "| 6102746 |     [5844971]      |\n",
       "| 2658191 |     [4079144]      |\n",
       "| 7839285 |     [7666083]      |\n",
       "| 1261821 | [8202919, 8202919] |\n",
       "|  504649 | [7377993, 5511390] |\n",
       "| 7392217 |     [8253334]      |\n",
       "| 1185276 |     [7899465]      |\n",
       "| 1968541 |     [4516159]      |\n",
       "+---------+--------------------+\n",
       "[10 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算每个节点在转发网络中的出度节点集合\n",
    "out_friends_sf = zhuanfa_links.groupby(\"src\", {\"out_friends2\": gl.aggregate.CONCAT(\"dst\")})\n",
    "out_friends_sf.rename({\"src\": \"id\"})\n",
    "out_friends_sf.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
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       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">in_friends2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "    </tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[7522772, 7988546,<br>7526782, 7854825, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6723084</td>\n",
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       "    </tr>\n",
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       "    </tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6983937</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[635740, 6243433,<br>4304196] ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6011377</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[5774459, 6431916]</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 2 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\tin_friends2\tlist\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+---------+-------------------------------+\n",
       "|    id   |          in_friends2          |\n",
       "+---------+-------------------------------+\n",
       "| 6693645 |           [6594948]           |\n",
       "| 7506315 |           [5584488]           |\n",
       "| 6310057 |           [7904313]           |\n",
       "| 7691878 |       [5525981, 5525981]      |\n",
       "| 6400323 |           [6400323]           |\n",
       "| 7622051 | [7522772, 7988546, 7526782... |\n",
       "| 6723084 |           [4077516]           |\n",
       "| 4644262 |           [4292987]           |\n",
       "| 6983937 |   [635740, 6243433, 4304196]  |\n",
       "| 6011377 |       [5774459, 6431916]      |\n",
       "+---------+-------------------------------+\n",
       "[10 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算每个节点在转发网络中的入度节点集合\n",
    "in_friends_sf = zhuanfa_links.groupby(\"dst\", {\"in_friends2\": gl.aggregate.CONCAT(\"src\")})\n",
    "in_friends_sf.rename({\"dst\": \"id\"})\n",
    "in_friends_sf.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
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       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">in_friends2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">out_friends2</th>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[7434393]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[109619]</td>\n",
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       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[5496360]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">27</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[8276870]</td>\n",
       "    </tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[5222540]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">33</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[1068229]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[5687616]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">52</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[468821]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">73</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[]</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[7017144]</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 3 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\tin_friends2\tlist\n",
       "\tout_friends2\tlist\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+----+-------------+--------------+\n",
       "| id | in_friends2 | out_friends2 |\n",
       "+----+-------------+--------------+\n",
       "| 5  |      []     |   [468821]   |\n",
       "| 7  |      []     |  [7434393]   |\n",
       "| 8  |      []     |   [109619]   |\n",
       "| 10 |      []     |  [5496360]   |\n",
       "| 27 |      []     |  [8276870]   |\n",
       "| 29 |      []     |  [5222540]   |\n",
       "| 33 |      []     |  [1068229]   |\n",
       "| 47 |      []     |  [5687616]   |\n",
       "| 52 |      []     |   [468821]   |\n",
       "| 73 |      []     |  [7017144]   |\n",
       "+----+-------------+--------------+\n",
       "[10 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用节点的ID将上述数据合并到一起\n",
    "users_G2 = users_G2.join(in_friends_sf, on=\"id\", how=\"outer\")\n",
    "users_G2 = users_G2.join(out_friends_sf, on=\"id\", how=\"outer\")\n",
    "\n",
    "#we replace missing values with empty lists\n",
    "users_G2 = users_G2.fillna('in_friends2',[])\n",
    "users_G2 = users_G2.fillna('out_friends2',[])\n",
    "users_G2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">in_friends2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">out_friends2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">out_degree2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">in_degree2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">all_friends2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">all_degree2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bi_friends2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bi_degree2</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[7017144]</td>\n",
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       "    </tr>\n",
       "</table>\n",
       "[10 rows x 9 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\tin_friends2\tlist\n",
       "\tout_friends2\tlist\n",
       "\tout_degree2\tint\n",
       "\tin_degree2\tint\n",
       "\tall_friends2\tarray\n",
       "\tall_degree2\tint\n",
       "\tbi_friends2\tlist\n",
       "\tbi_degree2\tint\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+----+-------------+--------------+-------------+------------+--------------+-------------+\n",
       "| id | in_friends2 | out_friends2 | out_degree2 | in_degree2 | all_friends2 | all_degree2 |\n",
       "+----+-------------+--------------+-------------+------------+--------------+-------------+\n",
       "| 5  |      []     |   [468821]   |      1      |     0      |  [468821.0]  |      1      |\n",
       "| 7  |      []     |  [7434393]   |      1      |     0      | [7434393.0]  |      1      |\n",
       "| 8  |      []     |   [109619]   |      1      |     0      |  [109619.0]  |      1      |\n",
       "| 10 |      []     |  [5496360]   |      1      |     0      | [5496360.0]  |      1      |\n",
       "| 27 |      []     |  [8276870]   |      1      |     0      | [8276870.0]  |      1      |\n",
       "| 29 |      []     |  [5222540]   |      1      |     0      | [5222540.0]  |      1      |\n",
       "| 33 |      []     |  [1068229]   |      1      |     0      | [1068229.0]  |      1      |\n",
       "| 47 |      []     |  [5687616]   |      1      |     0      | [5687616.0]  |      1      |\n",
       "| 52 |      []     |   [468821]   |      1      |     0      |  [468821.0]  |      1      |\n",
       "| 73 |      []     |  [7017144]   |      1      |     0      | [7017144.0]  |      1      |\n",
       "+----+-------------+--------------+-------------+------------+--------------+-------------+\n",
       "+-------------+------------+\n",
       "| bi_friends2 | bi_degree2 |\n",
       "+-------------+------------+\n",
       "|      []     |     0      |\n",
       "|      []     |     0      |\n",
       "|      []     |     0      |\n",
       "|      []     |     0      |\n",
       "|      []     |     0      |\n",
       "|      []     |     0      |\n",
       "|      []     |     0      |\n",
       "|      []     |     0      |\n",
       "|      []     |     0      |\n",
       "|      []     |     0      |\n",
       "+-------------+------------+\n",
       "[10 rows x 9 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#out_degree - number of users each vertex is following\n",
    "users_G2['out_degree2'] = users_G2[\"out_friends2\"].apply(lambda l: len(l) )\n",
    "\n",
    "#in_degree - number of users following each vertex\n",
    "users_G2['in_degree2'] = users_G2[\"in_friends2\"].apply(lambda l: len(l) )\n",
    "\n",
    "#all_degree - number of uniuqe users that following or are followed by each user\n",
    "users_G2['all_friends2'] = users_G2[['in_friends2', 'out_friends2']].apply(lambda r: list(set(r['in_friends2']) | set(r['out_friends2'])))\n",
    "users_G2['all_degree2'] = users_G2[\"all_friends2\"].apply(lambda l: len(l) )\n",
    "\n",
    "#bi_degree - number of uniuqe users that are both following and followed by each user\n",
    "users_G2['bi_friends2'] = users_G2[['in_friends2', 'out_friends2']].apply(lambda r: list(set(r['in_friends2']) & set(r['out_friends2'])))\n",
    "users_G2['bi_degree2'] = users_G2[\"bi_friends2\"].apply(lambda l: len(l) )\n",
    "\n",
    "users_G2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">out_degree2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">in_degree2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">all_degree2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bi_degree2</th>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">27</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">33</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">52</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">73</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 5 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\tout_degree2\tint\n",
       "\tin_degree2\tint\n",
       "\tall_degree2\tint\n",
       "\tbi_degree2\tint\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+----+-------------+------------+-------------+------------+\n",
       "| id | out_degree2 | in_degree2 | all_degree2 | bi_degree2 |\n",
       "+----+-------------+------------+-------------+------------+\n",
       "| 5  |      1      |     0      |      1      |     0      |\n",
       "| 7  |      1      |     0      |      1      |     0      |\n",
       "| 8  |      1      |     0      |      1      |     0      |\n",
       "| 10 |      1      |     0      |      1      |     0      |\n",
       "| 27 |      1      |     0      |      1      |     0      |\n",
       "| 29 |      1      |     0      |      1      |     0      |\n",
       "| 33 |      1      |     0      |      1      |     0      |\n",
       "| 47 |      1      |     0      |      1      |     0      |\n",
       "| 52 |      1      |     0      |      1      |     0      |\n",
       "| 73 |      1      |     0      |      1      |     0      |\n",
       "+----+-------------+------------+-------------+------------+\n",
       "[10 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del users_G2['in_friends2']\n",
    "del users_G2['out_friends2']\n",
    "del users_G2['all_friends2']\n",
    "del users_G2['bi_friends2']\n",
    "users_G2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wCDeAE1qzBcCTnW7bW20BsK1T39Zq+/SpqheBPUmOf5WxJEnSFJoz7AlIkiS9XknewuBu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\nxSAl75WqehL4BPA9Bq+0fLaq7hjtrGbd3wL/JMnSJAcy+D8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1142fb6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = gcf() \n",
    "fig.set_size_inches(18, 12)\n",
    "\n",
    "subplot(2,2,1)\n",
    "out_degree=users_G2['out_degree2']\n",
    "hist(out_degree,range(1,10))\n",
    "xlabel('out_degree')\n",
    "\n",
    "subplot(2,2,2)\n",
    "in_degree=users_G2['in_degree2']\n",
    "hist(in_degree,range(1,10))\n",
    "xlabel('in_degree')\n",
    "\n",
    "subplot(2,2,3)\n",
    "all_degree=users_G2['all_degree2']\n",
    "hist(all_degree,range(1,10))\n",
    "xlabel('all_degree')\n",
    "\n",
    "subplot(2,2,4)\n",
    "bi_degree=users_G2['bi_degree2']\n",
    "hist(bi_degree,range(1,10))\n",
    "xlabel('bi_degree')\n",
    "\n",
    "savefig(\"network2_degree.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Counting out degree</pre>"
      ],
      "text/plain": [
       "Counting out degree"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Done counting out degree</pre>"
      ],
      "text/plain": [
       "Done counting out degree"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+-----------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+-----------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | L1 change in pagerank |</pre>"
      ],
      "text/plain": [
       "| Iteration | L1 change in pagerank |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+-----------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+-----------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 1.21098e+07           |</pre>"
      ],
      "text/plain": [
       "| 1         | 1.21098e+07           |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 4.82269e+06           |</pre>"
      ],
      "text/plain": [
       "| 2         | 4.82269e+06           |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 1.80322e+06           |</pre>"
      ],
      "text/plain": [
       "| 3         | 1.80322e+06           |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 801615                |</pre>"
      ],
      "text/plain": [
       "| 4         | 801615                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5         | 402386                |</pre>"
      ],
      "text/plain": [
       "| 5         | 402386                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6         | 233994                |</pre>"
      ],
      "text/plain": [
       "| 6         | 233994                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7         | 149408                |</pre>"
      ],
      "text/plain": [
       "| 7         | 149408                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 8         | 101659                |</pre>"
      ],
      "text/plain": [
       "| 8         | 101659                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 9         | 72190.5               |</pre>"
      ],
      "text/plain": [
       "| 9         | 72190.5               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10        | 52508.1               |</pre>"
      ],
      "text/plain": [
       "| 10        | 52508.1               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11        | 38967.6               |</pre>"
      ],
      "text/plain": [
       "| 11        | 38967.6               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 12        | 29449.8               |</pre>"
      ],
      "text/plain": [
       "| 12        | 29449.8               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 13        | 22513.8               |</pre>"
      ],
      "text/plain": [
       "| 13        | 22513.8               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 14        | 17451.7               |</pre>"
      ],
      "text/plain": [
       "| 14        | 17451.7               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 15        | 13621.7               |</pre>"
      ],
      "text/plain": [
       "| 15        | 13621.7               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 16        | 10758.7               |</pre>"
      ],
      "text/plain": [
       "| 16        | 10758.7               |"
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       "[10 rows x 6 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\tout_degree2\tint\n",
       "\tin_degree2\tint\n",
       "\tall_degree2\tint\n",
       "\tbi_degree2\tint\n",
       "\tpagerank2\tfloat\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+----+-------------+------------+-------------+------------+-----------+\n",
       "| id | out_degree2 | in_degree2 | all_degree2 | bi_degree2 | pagerank2 |\n",
       "+----+-------------+------------+-------------+------------+-----------+\n",
       "| 5  |      1      |     0      |      1      |     0      |    0.15   |\n",
       "| 7  |      1      |     0      |      1      |     0      |    0.15   |\n",
       "| 8  |      1      |     0      |      1      |     0      |    0.15   |\n",
       "| 10 |      1      |     0      |      1      |     0      |    0.15   |\n",
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       "| 29 |      1      |     0      |      1      |     0      |    0.15   |\n",
       "| 33 |      1      |     0      |      1      |     0      |    0.15   |\n",
       "| 47 |      1      |     0      |      1      |     0      |    0.15   |\n",
       "| 52 |      1      |     0      |      1      |     0      |    0.15   |\n",
       "| 73 |      1      |     0      |      1      |     0      |    0.15   |\n",
       "+----+-------------+------------+-------------+------------+-----------+\n",
       "[10 rows x 6 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 计算出每一个节点的pagerank值\n",
    "pr2 = gl.pagerank.create(G2)\n",
    "users_G2['pagerank2'] = pr2['graph'].vertices['pagerank'] #add the new pagerank field to the original graph g2\n",
    "users_G2.head(10)"
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  },
  {
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   "execution_count": 60,
   "metadata": {
    "collapsed": false
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   "outputs": [
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     "data": {
      "text/html": [
       "<pre>Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/users_G2.csv</pre>"
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       "Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/users_G2.csv"
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      "text/html": [
       "<pre>Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/users_G2.csv</pre>"
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       "Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/users_G2.csv"
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     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[int,int,int,int,int,float]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
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       "    </tr>\n",
       "</table>\n",
       "[10 rows x 6 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\ttweet_user\tint\n",
       "\tout_degree2\tint\n",
       "\tin_degree2\tint\n",
       "\tall_degree2\tint\n",
       "\tbi_degree2\tint\n",
       "\tpagerank2\tfloat\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+------------+-------------+------------+-------------+------------+-----------+\n",
       "| tweet_user | out_degree2 | in_degree2 | all_degree2 | bi_degree2 | pagerank2 |\n",
       "+------------+-------------+------------+-------------+------------+-----------+\n",
       "|     5      |      1      |     0      |      1      |     0      |    0.15   |\n",
       "|     7      |      1      |     0      |      1      |     0      |    0.15   |\n",
       "|     8      |      1      |     0      |      1      |     0      |    0.15   |\n",
       "|     10     |      1      |     0      |      1      |     0      |    0.15   |\n",
       "|     27     |      1      |     0      |      1      |     0      |    0.15   |\n",
       "|     29     |      1      |     0      |      1      |     0      |    0.15   |\n",
       "|     33     |      1      |     0      |      1      |     0      |    0.15   |\n",
       "|     47     |      1      |     0      |      1      |     0      |    0.15   |\n",
       "|     52     |      1      |     0      |      1      |     0      |    0.15   |\n",
       "|     73     |      1      |     0      |      1      |     0      |    0.15   |\n",
       "+------------+-------------+------------+-------------+------------+-----------+\n",
       "[10 rows x 6 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将其保存为csv文件以便今后使用\n",
    "#users_G2.rename({'id': 'tweet_user'})                              #更改为tweet_user以便进行数据连接\n",
    "#users_G2.save('users_G2.csv', format='csv')                        #可提供该数据users_G2\n",
    "\n",
    "#使用graphlab重新将数据载入到内存空间中 \n",
    "users_G2=gl.SFrame.read_csv('users_G2.csv')\n",
    "users_G2.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5 构建每一条微博传播的级联树结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5.1 为构建级联传播树进行数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26992"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#采用pandas框架读入数据\n",
    "trainRepost=pd.read_csv('trainRepost_sort.csv')\n",
    "\n",
    "#生成一个所有微博编号的列表\n",
    "weibo_id_list=trainRepost.weibo_id\n",
    "\n",
    "#提取列表中有效元素（重复部分数据只取一个），防止创建树时重复创建节点\n",
    "weibo_id_list=list(np.unique(weibo_id_list))\n",
    "\n",
    "#去除个别格式有问题的微博数据\n",
    "weibo_id_list.remove(3793991852126437)\n",
    "weibo_id_list.remove(3793992178990261)\n",
    "\n",
    "#这个列表的长度就是有转发级联树微博的数量\n",
    "len(weibo_id_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##将其保存为pkl文件以便今后使用\n",
    "#pickle_file=open('weibo_id_list.pkl','w')\n",
    "#pickle.dump(weibo_id_list,pickle_file)                         #可提供该数据weibo_id_list           \n",
    "#pickle_file.close() \n",
    "\n",
    "#采cpickle函数重新读入weibo_id_list数据\n",
    "pickle_file=open('weibo_id_list.pkl','r')\n",
    "weibo_id_list=pickle.load(pickle_file)\n",
    "pickle_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 传播树的例子\n",
    "![alt text](spreading_tree.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5.2 微博传播深度计算的具体说明\n",
    "\n",
    "以源微博的用户为起点，计算转发链上其他节点到源节点的距离，最长距离就是此微博的传播深度。\n",
    "\n",
    "举个例子，假如源微博用户是a，用户b从a处转发了微博，用户c从b处转发了微博，用户d又从c处转发了微博，那么微博传播就是a->b->c->d，深度就为3。\n",
    "\n",
    "但是需要注意三点。\n",
    "\n",
    "- 第一点：一个用户可能从多个源都做转发，比如在之后的时间里，c从d处进行了转发，或者a又从c处做了转发，在这种情况下，深度并不增加；同样的，若源微博用户a再次转发了自己的微博，深度也不增加。 \n",
    "\n",
    "- 第二点：若在转发链为a->b->c->d中，出现了新的用户e，从a转发了微博，然后又从d处转发了一次：对于这种情况，我们认为转发链的深度并没有增加，因为e已经被认定为距离a只有1步，此时再从d处转发，并没有实际深度的增加。但需注意，若e先从d处转发，此时传播深度便需要加1，之后e再从a处转发，深度不会降低。 \n",
    "\n",
    "- 第三点：在数据采集过程中，由于各种原因（比如新浪自动截断、用户手动删除、采集遗漏等），转发链可能出现中断，继续采用上面的例子，假如用户e可能从a、b、c或d中做了转发，但是训练集中没有这一纪录，反倒是有用户f从e处转发了此微博，也就是说无法得知用户e的父亲节点，导致深度计算出现问题。为解决这一问题，我们在计算深度的时候，默认添加从a到e的转发关系，于是从a到e的距离就为1，到f的距离为2。\n",
    "\n",
    "需要指出的是：在数据采集过程中，由于一些原因可能会出现少量数据遗失的情形，比如 \n",
    "- (a)用户关注关系的遗失，新产生的关系不能更新 \n",
    "- (b)在微博转发过程中的用户，有小部分不在用户关注关系数据中 \n",
    "- (c)转发链可能断裂，比如源微博用户为a，转发链为b转发了a，c转发了b，d转发了c，信息走向就为a->b->c->d，此时深度应为a到d的距离3，但在采集过程中，可能会漏掉b->c这一段，从而导致转发链断裂。此时在计算传播深度的时候，会将转发链视为a->b和a->c->d，深度就计算就取节点a与其他节点的最长距离，即从a到d，深度为2.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#生成每一条级联传播树的核心代码\n",
    "def spreading_tree(spread_data):\n",
    "#在生成树形结构以前，应保证原始数据已经进行了排序操作\n",
    "    parent = list(spread_data['tweet_user'])\n",
    "    child = list(spread_data['retweet_user'])\n",
    "    stime=list(spread_data['spread_time'])\n",
    "\n",
    "    #创建一棵树\n",
    "    node_tree=Tree()\n",
    "    \n",
    "    #创建根节点\n",
    "    node_tree.create_node(parent[0],parent[0],data=0)\n",
    "      \n",
    "    for i in range(len(spread_data)):\n",
    "        if node_tree.contains(parent[i]):\n",
    "            try:\n",
    "                #使用try语句，在父节点相同时，试图创建子节点，若可以创建则创建为子节点，若重复传播无法创建则直接循环下一条语句\n",
    "                node_tree.create_node(child[i],child[i],parent=parent[i],data=stime[i])\n",
    "            except:\n",
    "                pass\n",
    "        else:\n",
    "            try:\n",
    "                #如父节点不在树中，则将父节点设置为根节点，解决传播链断裂的情形\n",
    "                #(c)转发链可能断裂，比如源微博用户为a，转发链为b转发了a，c转发了b，d转发了c，信息走向就为a->b->c->d，\n",
    "                #此时深度应为a到d的距离3，但在采集过程中，可能会漏掉b->c这一段，从而导致转发链断裂。此时在计算传播深度的时候，\n",
    "                #会将转发链视为a->b和a->c->d，深度就计算就取节点a与其他节点的最长距离，即从a到d，深度为2。\n",
    "                node_tree.create_node(parent[i],parent[i],parent=parent[0],data=stime[i])\n",
    "                node_tree.create_node(child[i],child[i],parent=parent[i],data=stime[i])\n",
    "            except:\n",
    "                pass\n",
    "                \n",
    "    return node_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#在遍历树的过程中求得传播特性的核心代码\n",
    "def spreading_results(tree_name):\n",
    "    #将树结构变成节点的字典形式\n",
    "    time_dict=tree_name.nodes\n",
    "    #得到一个键值列表\n",
    "    keys_list=time_dict.keys()\n",
    "    \n",
    "    spread_scale=0\n",
    "    spread_depth=0\n",
    "    scale_list=[]\n",
    "    depth_list=[]\n",
    "    spread_list=[]\n",
    "    \n",
    "    range_scale=range(1,61,1)\n",
    "    range_scale.extend(range(75,72*61+1,15))\n",
    "    for time_range in range_scale:\n",
    "        for item in keys_list:\n",
    "            spread_user_list=[]\n",
    "            if time_dict[item].data<=time_range*60:\n",
    "                spread_user_list.append(item)\n",
    "                #将此时间从key_list中移除\n",
    "                keys_list.remove(item)\n",
    "\n",
    "            if len(spread_user_list)>0:\n",
    "                for retweet_user in spread_user_list:\n",
    "                    #逐一比较得出最深的树\n",
    "                    if tree_name.depth(retweet_user)>spread_depth:\n",
    "                        spread_depth=tree_name.depth(retweet_user)\n",
    "                        \n",
    "                #规模为各个时间点的节点长度和\n",
    "                node_length=len(spread_user_list)\n",
    "                spread_scale=spread_scale+node_length                                \n",
    "\n",
    "        scale_list.append(spread_scale-1)\n",
    "        depth_list.append(spread_depth)\n",
    "\n",
    "    spread_list=scale_list                                     #返回传播的规模\n",
    "    spread_list.extend(depth_list)                             #返回传播的深度\n",
    "    return spread_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6 转发关系中时间特征的具体实现"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6.1 提交数据CSV文件的说明\n",
    "以下均是在60-4380分钟(72小时)内数据进行处理，一方面是作为训练的目标函数用，另一方面在线提交的时候也要有这些结果。\n",
    "\n",
    "- 提交CSV结果文件，文档中包含每一条微博在不同时刻的传播规模和传播深度。 \n",
    "- CSV每行字段说明：测试微博id,t1传播规模,t2传播规模,…,tk传播规模,t1传播深度,t2传播深度,…,tk传播深度 \n",
    "- 每一行为一个微博在某些时刻的传播规模和传播深度。 \n",
    "- 每列之间逗号分隔，每行之间回车换行。 \n",
    "- 微博ID的序号，逐行递增；同一微博ID，预测时刻逐列递增。 \n",
    "- 本次预测的时间间隔为15分钟，预测未来72小时的转发规模增长和传播深度增长。即从微博发出后的第75分钟到第4380分钟，每记录15分钟时的规模和深度，即测试集中有t1＝75，t2=90，......tk＝4380，一共288个时间点。（是4380，不是288，288只是考察的时间点的个数，从75到4380一共是288个时间点，表头也不要删掉）\n",
    "\n",
    "示例如下，包括表头（第一行），以及从第1条测试微博到第3000条测试微博：\n",
    "\n",
    "WeiboID (Time Unit: Minutes),scaleT75,scaleT90…,scaleT4380,depthT75,…,depthT4380 \n",
    "\n",
    "testWeibo1,100,110,…,400,3,…,5 \n",
    "\n",
    "testWeibo2,15,32,...,720,3,…,12 \n",
    "\n",
    "… \n",
    "\n",
    "testWeibo3000,221,332,...,3200,3,…,14 \n",
    "\n",
    "注：提交格式一定要与上述格式一致，才能正确计算得分 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6.2 训练所需时间特征格式的说明 \n",
    "以下均是在60分钟内数据进行处理，主要是放到训练集中做特征用的。\n",
    "\n",
    "- 每行字段说明：测试微博id,t1传播规模,t2传播规模,…,tk传播规模,t1传播深度,t2传播深度,…,tk传播深度 \n",
    "- 每一行为一个微博在某些时刻的传播规模和传播深度。 \n",
    "- 每列之间逗号分隔，每行之间回车换行。\n",
    "- 1-60分钟时间内转发的规模：WeiboID,scaleT1,scaleT2…,scaleT60；\n",
    "- 1-60分钟时间内转发的深度：WeiboID,depthT1,depthT2…,depthT60；\n",
    "- 1-60分钟时间内转发的宽度：WeiboID,widthT1,widthT2…,widthT60；\n",
    "- 60分钟内的变异系数：每分钟内转发量的标准差/每分钟内转发量的均值；\n",
    "- 60分钟内的峰值比率：每分钟内的最大转发量/总转发量；\n",
    "- 60分钟内的峰值时间：从信息出现到扩散峰值之间的时间；\n",
    "- 60分钟内的衰减时间：从峰值时间到信息扩散达到扩散规模75%所需的时间；"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6.2 转发关系中时间特征的具体实现过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 697 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           weibo_id scaleT1 scaleT2 scaleT3 scaleT4 scaleT5 scaleT6 scaleT7  \\\n",
       "0  3793992191673761     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1  3793996868052645     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2  3793997917454642     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3  3793999469290835     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "4  3794000245281644     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "  scaleT8 scaleT9    ...     depthT4245 depthT4260 depthT4275 depthT4290  \\\n",
       "0     NaN     NaN    ...            NaN        NaN        NaN        NaN   \n",
       "1     NaN     NaN    ...            NaN        NaN        NaN        NaN   \n",
       "2     NaN     NaN    ...            NaN        NaN        NaN        NaN   \n",
       "3     NaN     NaN    ...            NaN        NaN        NaN        NaN   \n",
       "4     NaN     NaN    ...            NaN        NaN        NaN        NaN   \n",
       "\n",
       "  depthT4305 depthT4320 depthT4335 depthT4350 depthT4365 depthT4380  \n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "\n",
       "[5 rows x 697 columns]"
      ]
     },
     "execution_count": 582,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#做出提交文件中表头的各种字段来，具体的格式如下：\n",
    "#WeiboID,scaleT1,scaleT2…,scaleT60,scaleT75,scaleT90…,scaleT4380,depthT1,depthT2…,depthT60,depthT75,…,depthT4380 \n",
    "column_names=[]\n",
    "column_names.append('weibo_id')\n",
    "data=[]\n",
    "range_scale=range(1,61,1)\n",
    "range_scale.extend(range(75,72*61+1,15))\n",
    "\n",
    "for time_range in range_scale:\n",
    "    column_names.append('scaleT'+str(time_range))\n",
    "for time_range in range_scale:\n",
    "    column_names.append('depthT'+str(time_range))\n",
    "    \n",
    "WeiboProfile_train_spreading=pd.DataFrame(data,columns=column_names)\n",
    "WeiboProfile_train_spreading['weibo_id']=weibo_id_list\n",
    "WeiboProfile_train_spreading.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#将数据按照weibo_id进行整合\n",
    "trainRepost_group=trainRepost.groupby(['weibo_id'])\n",
    "\n",
    "ID_index=0\n",
    "for weibo_id in weibo_id_list:\n",
    "#全部处理时间很长，所以这里面可以设置仅显示处理几条数据\n",
    "for weibo_id in weibo_id_list:\n",
    "    if ID_index%1000==0:\n",
    "        print ID_index\n",
    "    weibo_id_spread_data=trainRepost_group.get_group(weibo_id)\n",
    "    tree_name=str(weibo_id)+'_node_tree'\n",
    "    tree_name=spreading_tree(weibo_id_spread_data)\n",
    "    weibo_id_spread_results=spreading_results(tree_name)\n",
    "    weibo_id_spread_results.insert(0,weibo_id)\n",
    "    WeiboProfile_train_spreading.ix[ID_index]=weibo_id_spread_results\n",
    "    ID_index=ID_index+1\n",
    "    \n",
    "    ##将树文件保存为pkl文件以便今后使用，合计会有2万多棵传播树\n",
    "    pickle_file=open('treedata/'+'tree_'+str(weibo_id)+'.pkl','w')            #可提供26992棵传播树数据\n",
    "    pickle.dump(tree_name,pickle_file)\n",
    "    pickle_file.close()\n",
    "    \n",
    "#查看最初的几条数据来验证结果\n",
    "WeiboProfile_train_spreading.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#将其保存为csv文件以便今后使用\n",
    "#该文件得到了每一个weino_id下的的传播情况，在每一时刻该树的深度，根节点被转发的数量\n",
    "#WeiboProfile_train_spreading.to_csv('WeiboProfile_train_spreading.csv',index=False)\n",
    "\n",
    "#将已经保存的文件读入到内存中\n",
    "WeiboProfile_train_spreading=pd.read_csv('WeiboProfile_train_spreading.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x120bee210>]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+eI677mrm5psbeOUrs45MkiRJkjRWJh40pXR397J06VVs2rSneeSxx+Y59thW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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11612de90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察几条微博的传播情况\n",
    "fig = gcf() \n",
    "fig.set_size_inches(18, 12)\n",
    "\n",
    "subplot(2,2,1)\n",
    "range_scale1=range(1,61,1)\n",
    "weibo1_result = WeiboProfile_train_spreading.loc[3]\n",
    "plot(range_scale1,weibo1_result[1:61],'bo-')\n",
    "\n",
    "subplot(2,2,2)\n",
    "range_scale2=range(75,72*61+1,15)\n",
    "plot(range_scale2,weibo1_result[61:349],'rs-')\n",
    "\n",
    "subplot(2,2,3)\n",
    "range_scale1=range(1,61,1)\n",
    "plot(range_scale1,weibo1_result[349:409],'bo-')\n",
    "\n",
    "subplot(2,2,4)\n",
    "range_scale2=range(75,72*61+1,15)\n",
    "plot(range_scale2,weibo1_result[409:697],'rs-')\n",
    "\n",
    "savefig(\"spreading_figure1.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x121acc810>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JkqSa4wKRkqRdvvjFFi68cAUvvXQ5IyN5mptz9Pa6OKRULRa2tRUWg/zpT+Ho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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1194b6350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察几条微博的传播情况\n",
    "fig = gcf() \n",
    "fig.set_size_inches(18, 12)\n",
    "\n",
    "subplot(2,2,1)\n",
    "range_scale1=range(1,61,1)\n",
    "weibo1_result = WeiboProfile_train_spreading.loc[0]\n",
    "plot(range_scale1,weibo1_result[1:61],'bo-')\n",
    "\n",
    "subplot(2,2,2)\n",
    "range_scale2=range(75,72*61+1,15)\n",
    "plot(range_scale2,weibo1_result[61:349],'rs-')\n",
    "\n",
    "subplot(2,2,3)\n",
    "range_scale1=range(1,61,1)\n",
    "plot(range_scale1,weibo1_result[349:409],'bo-')\n",
    "\n",
    "subplot(2,2,4)\n",
    "range_scale2=range(75,72*61+1,15)\n",
    "plot(range_scale2,weibo1_result[409:697],'rs-')\n",
    "\n",
    "savefig(\"spreading_figure2.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#读入weibo_id_list数据\n",
    "pickle_file=open('weibo_id_list.pkl','r')\n",
    "weibo_id_list=pickle.load(pickle_file)\n",
    "pickle_file.close()\n",
    "\n",
    "ID_index=0\n",
    "#读入结构树数据到内存中\n",
    "createvar = locals()\n",
    "for weibo_id in weibo_id_list:\n",
    "    if ID_index%1000==0:      #监测读入的速度\n",
    "        print ID_index\n",
    "    ID_index=ID_index+1\n",
    "    pickle_file=open('treedata/'+'tree_'+str(weibo_id)+'.pkl','r')\n",
    "    varname='tree_'+str(weibo_id)\n",
    "    createvar[varname]=pickle.load(pickle_file)\n",
    "    pickle_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#读入weibo_id_list数据\n",
    "pickle_file=open('weibo_id_list.pkl','r')\n",
    "weibo_id_list=pickle.load(pickle_file)\n",
    "pickle_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 微博转发内容和发布时间特征的构建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "源微博内容（含微博id与内容的对应关系），文件名为WeiboProfile.train \n",
    "\n",
    "在例子中，微博的内容就会记录为   \n",
    "微博id\\001微博作者id\\001微博发布时刻\\001微博内容   \n",
    "weibo1\\001小赵id\\00107:15:10\\001早上好   \n",
    "说明：微博发布时刻为当日的时间点，隐藏了日期，并使用24小时计时法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 微博发布时间特征的构建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weibo_id</th>\n",
       "      <th>tweet_user</th>\n",
       "      <th>release_time</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3794305741726764</td>\n",
       "      <td>2724513</td>\n",
       "      <td>20:47:26</td>\n",
       "      <td>看到这条微博的人，羊年都会走大运！</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3794545218812248</td>\n",
       "      <td>7460165</td>\n",
       "      <td>12:39:02</td>\n",
       "      <td>2015你最想获得下面那一项?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3794726233244929</td>\n",
       "      <td>7387806</td>\n",
       "      <td>00:38:19</td>\n",
       "      <td>从剧组偷了个闲出来认真的看过沙果“贱”入佳境了，幽默精彩，点赞支持👍，票房小黑马马力十足�...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3794986804085757</td>\n",
       "      <td>8091415</td>\n",
       "      <td>17:53:44</td>\n",
       "      <td>Hey,从事教师这一职业的我不甘于现状，心中总有股劲儿想要挑战自己！一次偶然的机会让我接触了...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3795261795455671</td>\n",
       "      <td>8189779</td>\n",
       "      <td>12:06:27</td>\n",
       "      <td>我操你们妈了隔壁[呵呵]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3795438593411753</td>\n",
       "      <td>7298946</td>\n",
       "      <td>23:48:59</td>\n",
       "      <td>[讨论]周杰伦的歌曲和正能量影响着几代人！当然还有现在爆红的00后组合TFBOYS的队长王俊...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3795659825938762</td>\n",
       "      <td>7900610</td>\n",
       "      <td>14:28:05</td>\n",
       "      <td>#2015我的开年心愿#圣诞已过，除夕未至。新年伊始，大家一定有新的心愿或者目标：收获一份甜...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3795746757084016</td>\n",
       "      <td>8276724</td>\n",
       "      <td>20:13:31</td>\n",
       "      <td>这个时代的基本特质，是虚假，乡愿，半真半假的文化。这是危险的。一个在低级的经济学功利主义下的...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3796681650863771</td>\n",
       "      <td>7333613</td>\n",
       "      <td>10:08:27</td>\n",
       "      <td>《启初婴儿多效倍润面 霜试用体验》我又来抛使用心得了,这次是上市一年多的新品牌“启初”，通过...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3796728916283233</td>\n",
       "      <td>8185330</td>\n",
       "      <td>13:16:16</td>\n",
       "      <td>一年了，真快啊！@刘恺威 @杨幂</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           weibo_id  tweet_user release_time  \\\n",
       "0  3794305741726764     2724513     20:47:26   \n",
       "1  3794545218812248     7460165     12:39:02   \n",
       "2  3794726233244929     7387806     00:38:19   \n",
       "3  3794986804085757     8091415     17:53:44   \n",
       "4  3795261795455671     8189779     12:06:27   \n",
       "5  3795438593411753     7298946     23:48:59   \n",
       "6  3795659825938762     7900610     14:28:05   \n",
       "7  3795746757084016     8276724     20:13:31   \n",
       "8  3796681650863771     7333613     10:08:27   \n",
       "9  3796728916283233     8185330     13:16:16   \n",
       "\n",
       "                                             content  \n",
       "0                                  看到这条微博的人，羊年都会走大运！  \n",
       "1                                    2015你最想获得下面那一项?  \n",
       "2  从剧组偷了个闲出来认真的看过沙果“贱”入佳境了，幽默精彩，点赞支持👍，票房小黑马马力十足�...  \n",
       "3  Hey,从事教师这一职业的我不甘于现状，心中总有股劲儿想要挑战自己！一次偶然的机会让我接触了...  \n",
       "4                                       我操你们妈了隔壁[呵呵]  \n",
       "5  [讨论]周杰伦的歌曲和正能量影响着几代人！当然还有现在爆红的00后组合TFBOYS的队长王俊...  \n",
       "6  #2015我的开年心愿#圣诞已过，除夕未至。新年伊始，大家一定有新的心愿或者目标：收获一份甜...  \n",
       "7  这个时代的基本特质，是虚假，乡愿，半真半假的文化。这是危险的。一个在低级的经济学功利主义下的...  \n",
       "8  《启初婴儿多效倍润面 霜试用体验》我又来抛使用心得了,这次是上市一年多的新品牌“启初”，通过...  \n",
       "9                                   一年了，真快啊！@刘恺威 @杨幂  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 将时分秒转化为小时表示，保存到WeiboProfile_train.csv中\n",
    "#为每一列起一个名字，方便今后的操作\n",
    "column_names=['weibo_id','tweet_user','release_time','content']\n",
    "\n",
    "#采用pandas框架读入数据\n",
    "WeiboProfile_train=pd.read_csv('WeiboProfile_train_origin.csv',header=0,names=column_names)\n",
    "WeiboProfile_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6JH8wyjqMz7HB5EFfkrR+Rt0S2A4cqKpDVfUIcCOwY8R1kCR1Rh0Cm4H7+94f7sokSWMw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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119beec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#将发微博的时间转化为xx.xx小时的形式\n",
    "new_time=[]\n",
    "for time_item in WeiboProfile_train['release_time']:\n",
    "    time_split=time_item.split(\":\")\n",
    "    new_time.append(float(time_split[0])+float(time_split[1])/60)\n",
    "WeiboProfile_train.release_time=new_time\n",
    "\n",
    "#对发微博的时间进行一个初步的分析\n",
    "fig = gcf() \n",
    "fig.set_size_inches(15, 9)\n",
    "hist(WeiboProfile_train.release_time,np.arange(0,24,1.0))\n",
    "xlabel('Hour')\n",
    "\n",
    "#将其保存为csv文件以便今后使用\n",
    "WeiboProfile_train.to_csv('WeiboProfile_train.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26998"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#这个列表的长度就是训练集样本的数量\n",
    "len(WeiboProfile_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 对训练集进行文本内容的分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import jieba \n",
    "words_list= []\n",
    "\n",
    "#处理训练集\n",
    "WeiboProfile_train=pd.read_csv('WeiboProfile_train.csv')\n",
    "for item in range(len(WeiboProfile_train)):                                           \n",
    "        line = str(WeiboProfile_train.content[item]).strip().decode('utf-8', 'ignore')        \n",
    "        pipei = re.compile(ur'[^\\u4e00-\\u9fa5]')          #用正则化匹配中文               \n",
    "        words = \" \".join(pipei.split(line)).strip()                      \n",
    "        words_list.append([j for j in jieba.cut(words)]) #将所有微博的词混合在一起"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#处理测试集\n",
    "WeiboProfile_test = pd.read_csv('WeiboProfile_test.csv')\n",
    "for item in range(len(WeiboProfile_test)):                                           \n",
    "        line = str(WeiboProfile_test.content[item]).strip().decode('utf-8', 'ignore')        \n",
    "        pipei = re.compile(ur'[^\\u4e00-\\u9fa5]')          #用正则化匹配中文               \n",
    "        words = \" \".join(pipei.split(line)).strip()                      \n",
    "        words_list.append([j for j in jieba.cut(words)]) #将所有微博的词混合在一起"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from gensim import corpora, models, similarities  \n",
    "dic = corpora.Dictionary(words_list)\n",
    "corpus = [dic.doc2bow(text) for text in words_list]\n",
    "tfidf = models.TfidfModel(corpus)\n",
    "corpus_tfidf = tfidf[corpus]\n",
    "lda = models.LdaModel(corpus_tfidf, id2word = dic, num_topics = 10)  #将微博内容分成10类\n",
    "corpus_lda = lda[corpus_tfidf]\n",
    "sum1 = len(WeiboProfile_train)\n",
    "\n",
    "#分别生成训练集内容的标签和测试集内容的标签\n",
    "train_category_label = [str(sorted(a,key=lambda x: x[1])[-1][0]) for a in corpus_lda[:sum1]]   #训练集内容的标签\n",
    "#test_category_label = [str(sorted(a,key=lambda x: x[1])[-1][0]) for a in corpus_lda[sum1:]]    #测试集内容的标签"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 需要构建的训练集文本特征\n",
    "\n",
    "- 微博的长度：wordlenth\n",
    "- 包含网址的数量：URL\n",
    "- 包含Hashtag的数量：Hashtag\n",
    "- @别人的数量：mention\n",
    "- 包含照片的数量：picture\n",
    "- 内容的类别：content_category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.4 训练集文本特征的构建过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weibo_id</th>\n",
       "      <th>tweet_user</th>\n",
       "      <th>release_time</th>\n",
       "      <th>content</th>\n",
       "      <th>wordlenth</th>\n",
       "      <th>URL</th>\n",
       "      <th>Hashtag</th>\n",
       "      <th>mention</th>\n",
       "      <th>picture</th>\n",
       "      <th>content_category</th>\n",
       "      <th>hongbao</th>\n",
       "      <th>guanzhu</th>\n",
       "      <th>choujiang</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>weibo_id</td>\n",
       "      <td>tweet_user</td>\n",
       "      <td>release_time</td>\n",
       "      <td>content</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3794305741726764</td>\n",
       "      <td>2724513</td>\n",
       "      <td>20.7833333333</td>\n",
       "      <td>看到这条微博的人，羊年都会走大运！</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3794545218812248</td>\n",
       "      <td>7460165</td>\n",
       "      <td>12.65</td>\n",
       "      <td>2015你最想获得下面那一项?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3794726233244929</td>\n",
       "      <td>7387806</td>\n",
       "      <td>0.633333333333</td>\n",
       "      <td>从剧组偷了个闲出来认真的看过沙果“贱”入佳境了，幽默精彩，点赞支持👍，票房小黑马马力十足�...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3794986804085757</td>\n",
       "      <td>8091415</td>\n",
       "      <td>17.8833333333</td>\n",
       "      <td>Hey,从事教师这一职业的我不甘于现状，心中总有股劲儿想要挑战自己！一次偶然的机会让我接触了...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3795261795455671</td>\n",
       "      <td>8189779</td>\n",
       "      <td>12.1</td>\n",
       "      <td>我操你们妈了隔壁[呵呵]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3795438593411753</td>\n",
       "      <td>7298946</td>\n",
       "      <td>23.8</td>\n",
       "      <td>[讨论]周杰伦的歌曲和正能量影响着几代人！当然还有现在爆红的00后组合TFBOYS的队长王俊...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3795659825938762</td>\n",
       "      <td>7900610</td>\n",
       "      <td>14.4666666667</td>\n",
       "      <td>#2015我的开年心愿#圣诞已过，除夕未至。新年伊始，大家一定有新的心愿或者目标：收获一份甜...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3795746757084016</td>\n",
       "      <td>8276724</td>\n",
       "      <td>20.2166666667</td>\n",
       "      <td>这个时代的基本特质，是虚假，乡愿，半真半假的文化。这是危险的。一个在低级的经济学功利主义下的...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3796681650863771</td>\n",
       "      <td>7333613</td>\n",
       "      <td>10.1333333333</td>\n",
       "      <td>《启初婴儿多效倍润面 霜试用体验》我又来抛使用心得了,这次是上市一年多的新品牌“启初”，通过...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           weibo_id  tweet_user    release_time  \\\n",
       "0          weibo_id  tweet_user    release_time   \n",
       "1  3794305741726764     2724513   20.7833333333   \n",
       "2  3794545218812248     7460165           12.65   \n",
       "3  3794726233244929     7387806  0.633333333333   \n",
       "4  3794986804085757     8091415   17.8833333333   \n",
       "5  3795261795455671     8189779            12.1   \n",
       "6  3795438593411753     7298946            23.8   \n",
       "7  3795659825938762     7900610   14.4666666667   \n",
       "8  3795746757084016     8276724   20.2166666667   \n",
       "9  3796681650863771     7333613   10.1333333333   \n",
       "\n",
       "                                             content  wordlenth  URL  Hashtag  \\\n",
       "0                                            content        NaN  NaN      NaN   \n",
       "1                                  看到这条微博的人，羊年都会走大运！        NaN  NaN      NaN   \n",
       "2                                    2015你最想获得下面那一项?        NaN  NaN      NaN   \n",
       "3  从剧组偷了个闲出来认真的看过沙果“贱”入佳境了，幽默精彩，点赞支持👍，票房小黑马马力十足�...        NaN  NaN      NaN   \n",
       "4  Hey,从事教师这一职业的我不甘于现状，心中总有股劲儿想要挑战自己！一次偶然的机会让我接触了...        NaN  NaN      NaN   \n",
       "5                                       我操你们妈了隔壁[呵呵]        NaN  NaN      NaN   \n",
       "6  [讨论]周杰伦的歌曲和正能量影响着几代人！当然还有现在爆红的00后组合TFBOYS的队长王俊...        NaN  NaN      NaN   \n",
       "7  #2015我的开年心愿#圣诞已过，除夕未至。新年伊始，大家一定有新的心愿或者目标：收获一份甜...        NaN  NaN      NaN   \n",
       "8  这个时代的基本特质，是虚假，乡愿，半真半假的文化。这是危险的。一个在低级的经济学功利主义下的...        NaN  NaN      NaN   \n",
       "9  《启初婴儿多效倍润面 霜试用体验》我又来抛使用心得了,这次是上市一年多的新品牌“启初”，通过...        NaN  NaN      NaN   \n",
       "\n",
       "   mention  picture  content_category  hongbao  guanzhu  choujiang  \n",
       "0      NaN      NaN               NaN      NaN      NaN        NaN  \n",
       "1      NaN      NaN               NaN      NaN      NaN        NaN  \n",
       "2      NaN      NaN               NaN      NaN      NaN        NaN  \n",
       "3      NaN      NaN               NaN      NaN      NaN        NaN  \n",
       "4      NaN      NaN               NaN      NaN      NaN        NaN  \n",
       "5      NaN      NaN               NaN      NaN      NaN        NaN  \n",
       "6      NaN      NaN               NaN      NaN      NaN        NaN  \n",
       "7      NaN      NaN               NaN      NaN      NaN        NaN  \n",
       "8      NaN      NaN               NaN      NaN      NaN        NaN  \n",
       "9      NaN      NaN               NaN      NaN      NaN        NaN  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names=['weibo_id','tweet_user','release_time','content']\n",
    "new_column_names=['wordlenth','URL','Hashtag','mention','picture','content_category','hongbao','guanzhu','choujiang']\n",
    "column_names=column_names+new_column_names\n",
    "WeiboProfile_train = pd.read_csv('WeiboProfile_train.csv',header = None,names=column_names)  #读取文件\n",
    "WeiboProfile_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#新特征1：微博长度\n",
    "def length(weibodata):\n",
    "    wordlength = []\n",
    "    for i in range(len(weibodata)):\n",
    "        wordlength.append(len(weibodata.content[i]))\n",
    "    return wordlength\n",
    "\n",
    "#新特征2：是否包含网址\n",
    "def URL(weibodata):\n",
    "    URL = []\n",
    "    for i in range(len(weibodata)):\n",
    "        if 'http' in weibodata.content[i]:\n",
    "            URL.append(1)\n",
    "        else:\n",
    "            URL.append(0)\n",
    "    return URL\n",
    "\n",
    "#新特征3：是否包含Hashtag\n",
    "def Hashtag(weibodata):\n",
    "    Hashtag = []\n",
    "    for i in range(len(weibodata)):\n",
    "        if weibodata.content[i].count('#') > 1:\n",
    "            Hashtag.append(1)\n",
    "        else:\n",
    "            Hashtag.append(0)\n",
    "    return Hashtag\n",
    "\n",
    "#新特征4：是否包含照片\n",
    "def picture(weibodata):\n",
    "    picture = []\n",
    "    for i in range(len(weibodata)):\n",
    "        if '【' in weibodata.content[i] or '图' in weibodata.content[i]:\n",
    "            picture.append(1)\n",
    "        else:\n",
    "            picture.append(0)\n",
    "    return picture\n",
    "\n",
    "#新特征5：已经提及别人\n",
    "def mention(weibodata):\n",
    "    mention = []\n",
    "    for i in range(len(weibodata)):\n",
    "        if '@' in weibodata.content[i]:\n",
    "            mention.append(1)\n",
    "        else:\n",
    "            mention.append(0)\n",
    "    return mention   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weibo_id</th>\n",
       "      <th>tweet_user</th>\n",
       "      <th>release_time</th>\n",
       "      <th>wordlenth</th>\n",
       "      <th>URL</th>\n",
       "      <th>Hashtag</th>\n",
       "      <th>mention</th>\n",
       "      <th>picture</th>\n",
       "      <th>content_category</th>\n",
       "      <th>hongbao</th>\n",
       "      <th>guanzhu</th>\n",
       "      <th>choujiang</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3794305741726764</td>\n",
       "      <td>2724513</td>\n",
       "      <td>20.7833333333</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3794545218812248</td>\n",
       "      <td>7460165</td>\n",
       "      <td>12.65</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3794726233244929</td>\n",
       "      <td>7387806</td>\n",
       "      <td>0.633333333333</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3794986804085757</td>\n",
       "      <td>8091415</td>\n",
       "      <td>17.8833333333</td>\n",
       "      <td>333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3795261795455671</td>\n",
       "      <td>8189779</td>\n",
       "      <td>12.1</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3795438593411753</td>\n",
       "      <td>7298946</td>\n",
       "      <td>23.8</td>\n",
       "      <td>359</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3795659825938762</td>\n",
       "      <td>7900610</td>\n",
       "      <td>14.4666666667</td>\n",
       "      <td>337</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3795746757084016</td>\n",
       "      <td>8276724</td>\n",
       "      <td>20.2166666667</td>\n",
       "      <td>405</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3796681650863771</td>\n",
       "      <td>7333613</td>\n",
       "      <td>10.1333333333</td>\n",
       "      <td>415</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3796728916283233</td>\n",
       "      <td>8185330</td>\n",
       "      <td>13.2666666667</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            weibo_id tweet_user    release_time  wordlenth  URL  Hashtag  \\\n",
       "1   3794305741726764    2724513   20.7833333333         51    0        0   \n",
       "2   3794545218812248    7460165           12.65         35    0        0   \n",
       "3   3794726233244929    7387806  0.633333333333        226    0        0   \n",
       "4   3794986804085757    8091415   17.8833333333        333    0        0   \n",
       "5   3795261795455671    8189779            12.1         32    0        0   \n",
       "6   3795438593411753    7298946            23.8        359    1        0   \n",
       "7   3795659825938762    7900610   14.4666666667        337    0        1   \n",
       "8   3795746757084016    8276724   20.2166666667        405    0        0   \n",
       "9   3796681650863771    7333613   10.1333333333        415    0        0   \n",
       "10  3796728916283233    8185330   13.2666666667         42    0        0   \n",
       "\n",
       "    mention  picture content_category  hongbao  guanzhu  choujiang  \n",
       "1         0        0                0        0        0          0  \n",
       "2         0        0                6        0        0          0  \n",
       "3         1        0                5        0        0          0  \n",
       "4         0        0                9        0        0          0  \n",
       "5         0        0                3        0        0          0  \n",
       "6         1        0                6        0        0          0  \n",
       "7         1        0                6        0        0          0  \n",
       "8         0        0                2        0        0          0  \n",
       "9         0        0                3        0        0          0  \n",
       "10        1        0                0        0        0          0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获取训练集的新字段\n",
    "Weibo_train.wordlenth = Weibo_train['content'].apply(lambda x: length(x))\n",
    "Weibo_train.URL = Weibo_train['content'].apply(lambda x: URL(x))\n",
    "Weibo_train.Hashtag = Weibo_train['content'].apply(lambda x: Hashtag(x))\n",
    "Weibo_train.picture = Weibo_train['content'].apply(lambda x: picture(x))\n",
    "Weibo_train.mention = Weibo_train['content'].apply(lambda x:mention(x))\n",
    "Weibo_train=Weibo_train.drop(0)\n",
    "Weibo_train.content_category=list(train_label)\n",
    "del Weibo_train['content']\n",
    "Weibo_train.head(10)\n",
    "\n",
    "#将其保存为csv文件以便今后使用\n",
    "WeiboProfile_train.to_csv('WeiboProfile_train_processed.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6 线下的训练过程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 训练集中各种特征的拼接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_user</th>\n",
       "      <th>out_degree2</th>\n",
       "      <th>in_degree2</th>\n",
       "      <th>all_degree2</th>\n",
       "      <th>bi_degree2</th>\n",
       "      <th>pagerank2</th>\n",
       "      <th>weibo_id</th>\n",
       "      <th>release_time</th>\n",
       "      <th>wordlenth</th>\n",
       "      <th>URL</th>\n",
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       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
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       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>144652</td>\n",
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       "      <td>920</td>\n",
       "      <td>1</td>\n",
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       "      <td>13.766667</td>\n",
       "      <td>96</td>\n",
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       "      <td>7</td>\n",
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       "      <td>144652</td>\n",
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       "      <td>3959404967835125</td>\n",
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       "      <td>173</td>\n",
       "      <td>0</td>\n",
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       "      <td>8</td>\n",
       "      <td>8</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155435</td>\n",
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       "      <td>385</td>\n",
       "      <td>380</td>\n",
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       "      <td>50.0258</td>\n",
       "      <td>3951263068328236</td>\n",
       "      <td>23.650000</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
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       "      <td>157223</td>\n",
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       "      <td>1096</td>\n",
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       "      <td>162</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>157223</td>\n",
       "      <td>0</td>\n",
       "      <td>1269</td>\n",
       "      <td>1096</td>\n",
       "      <td>0</td>\n",
       "      <td>82.6330</td>\n",
       "      <td>3963034382906252</td>\n",
       "      <td>11.233333</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>157223</td>\n",
       "      <td>0</td>\n",
       "      <td>1269</td>\n",
       "      <td>1096</td>\n",
       "      <td>0</td>\n",
       "      <td>82.6330</td>\n",
       "      <td>3963022601009002</td>\n",
       "      <td>10.466667</td>\n",
       "      <td>296</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>157245</td>\n",
       "      <td>0</td>\n",
       "      <td>2026</td>\n",
       "      <td>1989</td>\n",
       "      <td>0</td>\n",
       "      <td>218.6380</td>\n",
       "      <td>3966639445423588</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>157245</td>\n",
       "      <td>0</td>\n",
       "      <td>2026</td>\n",
       "      <td>1989</td>\n",
       "      <td>0</td>\n",
       "      <td>218.6380</td>\n",
       "      <td>3956326495593412</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>67</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 718 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tweet_user  out_degree2  in_degree2  all_degree2  bi_degree2  pagerank2  \\\n",
       "0      114011            0         255          252           0    14.1023   \n",
       "1      144652            7        1009          920           1    75.5127   \n",
       "2      144652            7        1009          920           1    75.5127   \n",
       "3      152768            0         547          543           0    77.5759   \n",
       "4      155435            0         385          380           0    50.0258   \n",
       "5      157223            0        1269         1096           0    82.6330   \n",
       "6      157223            0        1269         1096           0    82.6330   \n",
       "7      157223            0        1269         1096           0    82.6330   \n",
       "8      157245            0        2026         1989           0   218.6380   \n",
       "9      157245            0        2026         1989           0   218.6380   \n",
       "\n",
       "           weibo_id  release_time  wordlenth  URL     ...      depthT4245  \\\n",
       "0  3974606387442802      9.616667        392    1     ...               6   \n",
       "1  3957274441700193     13.766667         96    1     ...               7   \n",
       "2  3959404967835125     10.866667        173    0     ...               8   \n",
       "3  3954110896652586     20.266667        249    0     ...               3   \n",
       "4  3951263068328236     23.650000         90    1     ...              10   \n",
       "5  3958367494594822     14.166667        162    0     ...               1   \n",
       "6  3963034382906252     11.233333         14    0     ...               1   \n",
       "7  3963022601009002     10.466667        296    0     ...               1   \n",
       "8  3966639445423588     10.000000         86    0     ...               2   \n",
       "9  3956326495593412     23.000000         67    0     ...               4   \n",
       "\n",
       "   depthT4260  depthT4275  depthT4290  depthT4305  depthT4320  depthT4335  \\\n",
       "0           6           6           6           6           6           6   \n",
       "1           7           7           7           7           7           7   \n",
       "2           8           8           8           8           8           8   \n",
       "3           3           3           3           3           3           3   \n",
       "4          10          10          10          10          10          10   \n",
       "5           1           1           1           1           1           1   \n",
       "6           1           1           1           1           1           1   \n",
       "7           1           1           1           1           1           1   \n",
       "8           2           2           2           2           2           2   \n",
       "9           4           4           4           4           4           4   \n",
       "\n",
       "   depthT4350  depthT4365  depthT4380  \n",
       "0           6           6           6  \n",
       "1           7           7           7  \n",
       "2           8           8           8  \n",
       "3           3           3           3  \n",
       "4          10          10          10  \n",
       "5           1           1           1  \n",
       "6           1           1           1  \n",
       "7           1           1           1  \n",
       "8           2           2           2  \n",
       "9           4           4           4  \n",
       "\n",
       "[10 rows x 718 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读入各种csv文件到内存空间\n",
    "#WeiboProfile_train_processed：每条微博对应的文本特征\n",
    "WeiboProfile_train_processed=pd.read_csv('WeiboProfile_train_processed.csv')\n",
    "\n",
    "#users_G2：每条微博首发用户对应的传播网络特征\n",
    "users_G2=pd.read_csv('users_G2.csv')\n",
    "\n",
    "#基于同一个用户作标示，将上述两个特征进行合并\n",
    "WeiboProfile_train_tmp1=pd.merge(users_G2,WeiboProfile_train_processed,on='tweet_user', how='inner')\n",
    "\n",
    "#users_G1：每条微博首发用户对应的结构网络特征\n",
    "users_G1=pd.read_csv('users_G1.csv')\n",
    "WeiboProfile_train_tmp=pd.merge(WeiboProfile_train_tmp1,users_G1,on='tweet_user', how='inner')\n",
    "\n",
    "#WeiboProfile_train_spreading：每一个weino_id下的的传播情况，在每一时刻该树的深度，根节点被转发的数量\n",
    "WeiboProfile_train_spreading=pd.read_csv('WeiboProfile_train_spreading.csv')\n",
    "\n",
    "#基于同一个微博作标示，将上述特征进行合并\n",
    "WeiboProfile_train_final=pd.merge(WeiboProfile_train_tmp,WeiboProfile_train_spreading,on='weibo_id', how='inner')\n",
    "\n",
    "#将其保存为csv文件以便今后使用\n",
    "WeiboProfile_train_final.to_csv('WeiboProfile_train_final.csv',index=False)\n",
    "WeiboProfile_train_final.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/WeiboProfile_train_final.csv</pre>"
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      "text/plain": [
       "Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/WeiboProfile_train_final.csv"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 100 lines in 0.795602 secs.</pre>"
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       "Parsing completed. Parsed 100 lines in 0.795602 secs."
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       "<pre>Read 25027 lines. Lines per second: 12396</pre>"
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       "Read 25027 lines. Lines per second: 12396"
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     "data": {
      "text/html": [
       "<pre>Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/WeiboProfile_train_final.csv</pre>"
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      "text/plain": [
       "Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/WeiboProfile_train_final.csv"
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      "text/html": [
       "<pre>Parsing completed. Parsed 26955 lines in 2.11722 secs.</pre>"
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      "text/plain": [
       "Parsing completed. Parsed 26955 lines in 2.11722 secs."
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[int,int,int,int,int,float,int,float,int,int,int,int,int,int,int,int,int,float,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">tweet_user</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">out_degree2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">in_degree2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">all_degree2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bi_degree2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">pagerank2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">weibo_id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">release_time</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">wordlenth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">114011</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">255</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9.61666666667</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">392</td>\n",
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       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">URL</th>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">279</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.32334</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">279</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">36.1523</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">33</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">36.1523</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">33</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9385</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">all_degree1</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bi_degree1</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT1</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT3</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT4</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT5</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT6</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT7</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT8</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">64</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">330</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">90</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">28</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">42</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">56</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">330</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">90</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2036</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1365</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9414</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9414</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT10</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT11</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT12</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT13</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT14</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT15</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT16</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT17</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">scaleT18</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">...</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">15</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">15</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">16</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">19</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">35</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">51</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">74</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">124</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">83</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">97</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">111</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">116</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">125</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">138</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">146</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">154</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">161</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">13</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">13</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">17</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">18</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">19</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">13</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">18</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">20</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">21</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">...</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 718 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\ttweet_user\tint\n",
       "\tout_degree2\tint\n",
       "\tin_degree2\tint\n",
       "\tall_degree2\tint\n",
       "\tbi_degree2\tint\n",
       "\tpagerank2\tfloat\n",
       "\tweibo_id\tint\n",
       "\trelease_time\tfloat\n",
       "\twordlenth\tint\n",
       "\tURL\tint\n",
       "\tHashtag\tint\n",
       "\tmention\tint\n",
       "\tpicture\tint\n",
       "\tcontent_category\tint\n",
       "\thongbao\tint\n",
       "\tguanzhu\tint\n",
       "\tchoujiang\tint\n",
       "\tpagerank1\tfloat\n",
       "\tout_degree1\tint\n",
       "\tin_degree1\tint\n",
       "\tall_degree1\tint\n",
       "\tbi_degree1\tint\n",
       "\tscaleT1\tint\n",
       "\tscaleT2\tint\n",
       "\tscaleT3\tint\n",
       "\tscaleT4\tint\n",
       "\tscaleT5\tint\n",
       "\tscaleT6\tint\n",
       "\tscaleT7\tint\n",
       "\tscaleT8\tint\n",
       "\tscaleT9\tint\n",
       "\tscaleT10\tint\n",
       "\tscaleT11\tint\n",
       "\tscaleT12\tint\n",
       "\tscaleT13\tint\n",
       "\tscaleT14\tint\n",
       "\tscaleT15\tint\n",
       "\tscaleT16\tint\n",
       "\tscaleT17\tint\n",
       "\tscaleT18\tint\n",
       "\tscaleT19\tint\n",
       "\tscaleT20\tint\n",
       "\tscaleT21\tint\n",
       "\tscaleT22\tint\n",
       "\tscaleT23\tint\n",
       "\tscaleT24\tint\n",
       "\tscaleT25\tint\n",
       "\tscaleT26\tint\n",
       "\tscaleT27\tint\n",
       "\tscaleT28\tint\n",
       "\tscaleT29\tint\n",
       "\tscaleT30\tint\n",
       "\tscaleT31\tint\n",
       "\tscaleT32\tint\n",
       "\tscaleT33\tint\n",
       "\tscaleT34\tint\n",
       "\tscaleT35\tint\n",
       "\tscaleT36\tint\n",
       "\tscaleT37\tint\n",
       "\tscaleT38\tint\n",
       "\tscaleT39\tint\n",
       "\tscaleT40\tint\n",
       "\tscaleT41\tint\n",
       "\tscaleT42\tint\n",
       "\tscaleT43\tint\n",
       "\tscaleT44\tint\n",
       "\tscaleT45\tint\n",
       "\tscaleT46\tint\n",
       "\tscaleT47\tint\n",
       "\tscaleT48\tint\n",
       "\tscaleT49\tint\n",
       "\tscaleT50\tint\n",
       "\tscaleT51\tint\n",
       "\tscaleT52\tint\n",
       "\tscaleT53\tint\n",
       "\tscaleT54\tint\n",
       "\tscaleT55\tint\n",
       "\tscaleT56\tint\n",
       "\tscaleT57\tint\n",
       "\tscaleT58\tint\n",
       "\tscaleT59\tint\n",
       "\tscaleT60\tint\n",
       "\tscaleT75\tint\n",
       "\tscaleT90\tint\n",
       "\tscaleT105\tint\n",
       "\tscaleT120\tint\n",
       "\tscaleT135\tint\n",
       "\tscaleT150\tint\n",
       "\tscaleT165\tint\n",
       "\tscaleT180\tint\n",
       "\tscaleT195\tint\n",
       "\tscaleT210\tint\n",
       "\tscaleT225\tint\n",
       "\tscaleT240\tint\n",
       "\tscaleT255\tint\n",
       "\tscaleT270\tint\n",
       "\tscaleT285\tint\n",
       "\tscaleT300\tint\n",
       "\tscaleT315\tint\n",
       "\tscaleT330\tint\n",
       "\tscaleT345\tint\n",
       "\tscaleT360\tint\n",
       "\tscaleT375\tint\n",
       "\tscaleT390\tint\n",
       "\tscaleT405\tint\n",
       "\tscaleT420\tint\n",
       "\tscaleT435\tint\n",
       "\tscaleT450\tint\n",
       "\tscaleT465\tint\n",
       "\tscaleT480\tint\n",
       "\tscaleT495\tint\n",
       "\tscaleT510\tint\n",
       "\tscaleT525\tint\n",
       "\tscaleT540\tint\n",
       "\tscaleT555\tint\n",
       "\tscaleT570\tint\n",
       "\tscaleT585\tint\n",
       "\tscaleT600\tint\n",
       "\tscaleT615\tint\n",
       "\tscaleT630\tint\n",
       "\tscaleT645\tint\n",
       "\tscaleT660\tint\n",
       "\tscaleT675\tint\n",
       "\tscaleT690\tint\n",
       "\tscaleT705\tint\n",
       "\tscaleT720\tint\n",
       "\tscaleT735\tint\n",
       "\tscaleT750\tint\n",
       "\tscaleT765\tint\n",
       "\tscaleT780\tint\n",
       "\tscaleT795\tint\n",
       "\tscaleT810\tint\n",
       "\tscaleT825\tint\n",
       "\tscaleT840\tint\n",
       "\tscaleT855\tint\n",
       "\tscaleT870\tint\n",
       "\tscaleT885\tint\n",
       "\tscaleT900\tint\n",
       "\tscaleT915\tint\n",
       "\tscaleT930\tint\n",
       "\tscaleT945\tint\n",
       "\tscaleT960\tint\n",
       "\tscaleT975\tint\n",
       "\tscaleT990\tint\n",
       "\tscaleT1005\tint\n",
       "\tscaleT1020\tint\n",
       "\tscaleT1035\tint\n",
       "\tscaleT1050\tint\n",
       "\tscaleT1065\tint\n",
       "\tscaleT1080\tint\n",
       "\tscaleT1095\tint\n",
       "\tscaleT1110\tint\n",
       "\tscaleT1125\tint\n",
       "\tscaleT1140\tint\n",
       "\tscaleT1155\tint\n",
       "\tscaleT1170\tint\n",
       "\tscaleT1185\tint\n",
       "\tscaleT1200\tint\n",
       "\tscaleT1215\tint\n",
       "\tscaleT1230\tint\n",
       "\tscaleT1245\tint\n",
       "\tscaleT1260\tint\n",
       "\tscaleT1275\tint\n",
       "\tscaleT1290\tint\n",
       "\tscaleT1305\tint\n",
       "\tscaleT1320\tint\n",
       "\tscaleT1335\tint\n",
       "\tscaleT1350\tint\n",
       "\tscaleT1365\tint\n",
       "\tscaleT1380\tint\n",
       "\tscaleT1395\tint\n",
       "\tscaleT1410\tint\n",
       "\tscaleT1425\tint\n",
       "\tscaleT1440\tint\n",
       "\tscaleT1455\tint\n",
       "\tscaleT1470\tint\n",
       "\tscaleT1485\tint\n",
       "\tscaleT1500\tint\n",
       "\tscaleT1515\tint\n",
       "\tscaleT1530\tint\n",
       "\tscaleT1545\tint\n",
       "\tscaleT1560\tint\n",
       "\tscaleT1575\tint\n",
       "\tscaleT1590\tint\n",
       "\tscaleT1605\tint\n",
       "\tscaleT1620\tint\n",
       "\tscaleT1635\tint\n",
       "\tscaleT1650\tint\n",
       "\tscaleT1665\tint\n",
       "\tscaleT1680\tint\n",
       "\tscaleT1695\tint\n",
       "\tscaleT1710\tint\n",
       "\tscaleT1725\tint\n",
       "\tscaleT1740\tint\n",
       "\tscaleT1755\tint\n",
       "\tscaleT1770\tint\n",
       "\tscaleT1785\tint\n",
       "\tscaleT1800\tint\n",
       "\tscaleT1815\tint\n",
       "\tscaleT1830\tint\n",
       "\tscaleT1845\tint\n",
       "\tscaleT1860\tint\n",
       "\tscaleT1875\tint\n",
       "\tscaleT1890\tint\n",
       "\tscaleT1905\tint\n",
       "\tscaleT1920\tint\n",
       "\tscaleT1935\tint\n",
       "\tscaleT1950\tint\n",
       "\tscaleT1965\tint\n",
       "\tscaleT1980\tint\n",
       "\tscaleT1995\tint\n",
       "\tscaleT2010\tint\n",
       "\tscaleT2025\tint\n",
       "\tscaleT2040\tint\n",
       "\tscaleT2055\tint\n",
       "\tscaleT2070\tint\n",
       "\tscaleT2085\tint\n",
       "\tscaleT2100\tint\n",
       "\tscaleT2115\tint\n",
       "\tscaleT2130\tint\n",
       "\tscaleT2145\tint\n",
       "\tscaleT2160\tint\n",
       "\tscaleT2175\tint\n",
       "\tscaleT2190\tint\n",
       "\tscaleT2205\tint\n",
       "\tscaleT2220\tint\n",
       "\tscaleT2235\tint\n",
       "\tscaleT2250\tint\n",
       "\tscaleT2265\tint\n",
       "\tscaleT2280\tint\n",
       "\tscaleT2295\tint\n",
       "\tscaleT2310\tint\n",
       "\tscaleT2325\tint\n",
       "\tscaleT2340\tint\n",
       "\tscaleT2355\tint\n",
       "\tscaleT2370\tint\n",
       "\tscaleT2385\tint\n",
       "\tscaleT2400\tint\n",
       "\tscaleT2415\tint\n",
       "\tscaleT2430\tint\n",
       "\tscaleT2445\tint\n",
       "\tscaleT2460\tint\n",
       "\tscaleT2475\tint\n",
       "\tscaleT2490\tint\n",
       "\tscaleT2505\tint\n",
       "\tscaleT2520\tint\n",
       "\tscaleT2535\tint\n",
       "\tscaleT2550\tint\n",
       "\tscaleT2565\tint\n",
       "\tscaleT2580\tint\n",
       "\tscaleT2595\tint\n",
       "\tscaleT2610\tint\n",
       "\tscaleT2625\tint\n",
       "\tscaleT2640\tint\n",
       "\tscaleT2655\tint\n",
       "\tscaleT2670\tint\n",
       "\tscaleT2685\tint\n",
       "\tscaleT2700\tint\n",
       "\tscaleT2715\tint\n",
       "\tscaleT2730\tint\n",
       "\tscaleT2745\tint\n",
       "\tscaleT2760\tint\n",
       "\tscaleT2775\tint\n",
       "\tscaleT2790\tint\n",
       "\tscaleT2805\tint\n",
       "\tscaleT2820\tint\n",
       "\tscaleT2835\tint\n",
       "\tscaleT2850\tint\n",
       "\tscaleT2865\tint\n",
       "\tscaleT2880\tint\n",
       "\tscaleT2895\tint\n",
       "\tscaleT2910\tint\n",
       "\tscaleT2925\tint\n",
       "\tscaleT2940\tint\n",
       "\tscaleT2955\tint\n",
       "\tscaleT2970\tint\n",
       "\tscaleT2985\tint\n",
       "\tscaleT3000\tint\n",
       "\tscaleT3015\tint\n",
       "\tscaleT3030\tint\n",
       "\tscaleT3045\tint\n",
       "\tscaleT3060\tint\n",
       "\tscaleT3075\tint\n",
       "\tscaleT3090\tint\n",
       "\tscaleT3105\tint\n",
       "\tscaleT3120\tint\n",
       "\tscaleT3135\tint\n",
       "\tscaleT3150\tint\n",
       "\tscaleT3165\tint\n",
       "\tscaleT3180\tint\n",
       "\tscaleT3195\tint\n",
       "\tscaleT3210\tint\n",
       "\tscaleT3225\tint\n",
       "\tscaleT3240\tint\n",
       "\tscaleT3255\tint\n",
       "\tscaleT3270\tint\n",
       "\tscaleT3285\tint\n",
       "\tscaleT3300\tint\n",
       "\tscaleT3315\tint\n",
       "\tscaleT3330\tint\n",
       "\tscaleT3345\tint\n",
       "\tscaleT3360\tint\n",
       "\tscaleT3375\tint\n",
       "\tscaleT3390\tint\n",
       "\tscaleT3405\tint\n",
       "\tscaleT3420\tint\n",
       "\tscaleT3435\tint\n",
       "\tscaleT3450\tint\n",
       "\tscaleT3465\tint\n",
       "\tscaleT3480\tint\n",
       "\tscaleT3495\tint\n",
       "\tscaleT3510\tint\n",
       "\tscaleT3525\tint\n",
       "\tscaleT3540\tint\n",
       "\tscaleT3555\tint\n",
       "\tscaleT3570\tint\n",
       "\tscaleT3585\tint\n",
       "\tscaleT3600\tint\n",
       "\tscaleT3615\tint\n",
       "\tscaleT3630\tint\n",
       "\tscaleT3645\tint\n",
       "\tscaleT3660\tint\n",
       "\tscaleT3675\tint\n",
       "\tscaleT3690\tint\n",
       "\tscaleT3705\tint\n",
       "\tscaleT3720\tint\n",
       "\tscaleT3735\tint\n",
       "\tscaleT3750\tint\n",
       "\tscaleT3765\tint\n",
       "\tscaleT3780\tint\n",
       "\tscaleT3795\tint\n",
       "\tscaleT3810\tint\n",
       "\tscaleT3825\tint\n",
       "\tscaleT3840\tint\n",
       "\tscaleT3855\tint\n",
       "\tscaleT3870\tint\n",
       "\tscaleT3885\tint\n",
       "\tscaleT3900\tint\n",
       "\tscaleT3915\tint\n",
       "\tscaleT3930\tint\n",
       "\tscaleT3945\tint\n",
       "\tscaleT3960\tint\n",
       "\tscaleT3975\tint\n",
       "\tscaleT3990\tint\n",
       "\tscaleT4005\tint\n",
       "\tscaleT4020\tint\n",
       "\tscaleT4035\tint\n",
       "\tscaleT4050\tint\n",
       "\tscaleT4065\tint\n",
       "\tscaleT4080\tint\n",
       "\tscaleT4095\tint\n",
       "\tscaleT4110\tint\n",
       "\tscaleT4125\tint\n",
       "\tscaleT4140\tint\n",
       "\tscaleT4155\tint\n",
       "\tscaleT4170\tint\n",
       "\tscaleT4185\tint\n",
       "\tscaleT4200\tint\n",
       "\tscaleT4215\tint\n",
       "\tscaleT4230\tint\n",
       "\tscaleT4245\tint\n",
       "\tscaleT4260\tint\n",
       "\tscaleT4275\tint\n",
       "\tscaleT4290\tint\n",
       "\tscaleT4305\tint\n",
       "\tscaleT4320\tint\n",
       "\tscaleT4335\tint\n",
       "\tscaleT4350\tint\n",
       "\tscaleT4365\tint\n",
       "\tscaleT4380\tint\n",
       "\tdepthT1\tint\n",
       "\tdepthT2\tint\n",
       "\tdepthT3\tint\n",
       "\tdepthT4\tint\n",
       "\tdepthT5\tint\n",
       "\tdepthT6\tint\n",
       "\tdepthT7\tint\n",
       "\tdepthT8\tint\n",
       "\tdepthT9\tint\n",
       "\tdepthT10\tint\n",
       "\tdepthT11\tint\n",
       "\tdepthT12\tint\n",
       "\tdepthT13\tint\n",
       "\tdepthT14\tint\n",
       "\tdepthT15\tint\n",
       "\tdepthT16\tint\n",
       "\tdepthT17\tint\n",
       "\tdepthT18\tint\n",
       "\tdepthT19\tint\n",
       "\tdepthT20\tint\n",
       "\tdepthT21\tint\n",
       "\tdepthT22\tint\n",
       "\tdepthT23\tint\n",
       "\tdepthT24\tint\n",
       "\tdepthT25\tint\n",
       "\tdepthT26\tint\n",
       "\tdepthT27\tint\n",
       "\tdepthT28\tint\n",
       "\tdepthT29\tint\n",
       "\tdepthT30\tint\n",
       "\tdepthT31\tint\n",
       "\tdepthT32\tint\n",
       "\tdepthT33\tint\n",
       "\tdepthT34\tint\n",
       "\tdepthT35\tint\n",
       "\tdepthT36\tint\n",
       "\tdepthT37\tint\n",
       "\tdepthT38\tint\n",
       "\tdepthT39\tint\n",
       "\tdepthT40\tint\n",
       "\tdepthT41\tint\n",
       "\tdepthT42\tint\n",
       "\tdepthT43\tint\n",
       "\tdepthT44\tint\n",
       "\tdepthT45\tint\n",
       "\tdepthT46\tint\n",
       "\tdepthT47\tint\n",
       "\tdepthT48\tint\n",
       "\tdepthT49\tint\n",
       "\tdepthT50\tint\n",
       "\tdepthT51\tint\n",
       "\tdepthT52\tint\n",
       "\tdepthT53\tint\n",
       "\tdepthT54\tint\n",
       "\tdepthT55\tint\n",
       "\tdepthT56\tint\n",
       "\tdepthT57\tint\n",
       "\tdepthT58\tint\n",
       "\tdepthT59\tint\n",
       "\tdepthT60\tint\n",
       "\tdepthT75\tint\n",
       "\tdepthT90\tint\n",
       "\tdepthT105\tint\n",
       "\tdepthT120\tint\n",
       "\tdepthT135\tint\n",
       "\tdepthT150\tint\n",
       "\tdepthT165\tint\n",
       "\tdepthT180\tint\n",
       "\tdepthT195\tint\n",
       "\tdepthT210\tint\n",
       "\tdepthT225\tint\n",
       "\tdepthT240\tint\n",
       "\tdepthT255\tint\n",
       "\tdepthT270\tint\n",
       "\tdepthT285\tint\n",
       "\tdepthT300\tint\n",
       "\tdepthT315\tint\n",
       "\tdepthT330\tint\n",
       "\tdepthT345\tint\n",
       "\tdepthT360\tint\n",
       "\tdepthT375\tint\n",
       "\tdepthT390\tint\n",
       "\tdepthT405\tint\n",
       "\tdepthT420\tint\n",
       "\tdepthT435\tint\n",
       "\tdepthT450\tint\n",
       "\tdepthT465\tint\n",
       "\tdepthT480\tint\n",
       "\tdepthT495\tint\n",
       "\tdepthT510\tint\n",
       "\tdepthT525\tint\n",
       "\tdepthT540\tint\n",
       "\tdepthT555\tint\n",
       "\tdepthT570\tint\n",
       "\tdepthT585\tint\n",
       "\tdepthT600\tint\n",
       "\tdepthT615\tint\n",
       "\tdepthT630\tint\n",
       "\tdepthT645\tint\n",
       "\tdepthT660\tint\n",
       "\tdepthT675\tint\n",
       "\tdepthT690\tint\n",
       "\tdepthT705\tint\n",
       "\tdepthT720\tint\n",
       "\tdepthT735\tint\n",
       "\tdepthT750\tint\n",
       "\tdepthT765\tint\n",
       "\tdepthT780\tint\n",
       "\tdepthT795\tint\n",
       "\tdepthT810\tint\n",
       "\tdepthT825\tint\n",
       "\tdepthT840\tint\n",
       "\tdepthT855\tint\n",
       "\tdepthT870\tint\n",
       "\tdepthT885\tint\n",
       "\tdepthT900\tint\n",
       "\tdepthT915\tint\n",
       "\tdepthT930\tint\n",
       "\tdepthT945\tint\n",
       "\tdepthT960\tint\n",
       "\tdepthT975\tint\n",
       "\tdepthT990\tint\n",
       "\tdepthT1005\tint\n",
       "\tdepthT1020\tint\n",
       "\tdepthT1035\tint\n",
       "\tdepthT1050\tint\n",
       "\tdepthT1065\tint\n",
       "\tdepthT1080\tint\n",
       "\tdepthT1095\tint\n",
       "\tdepthT1110\tint\n",
       "\tdepthT1125\tint\n",
       "\tdepthT1140\tint\n",
       "\tdepthT1155\tint\n",
       "\tdepthT1170\tint\n",
       "\tdepthT1185\tint\n",
       "\tdepthT1200\tint\n",
       "\tdepthT1215\tint\n",
       "\tdepthT1230\tint\n",
       "\tdepthT1245\tint\n",
       "\tdepthT1260\tint\n",
       "\tdepthT1275\tint\n",
       "\tdepthT1290\tint\n",
       "\tdepthT1305\tint\n",
       "\tdepthT1320\tint\n",
       "\tdepthT1335\tint\n",
       "\tdepthT1350\tint\n",
       "\tdepthT1365\tint\n",
       "\tdepthT1380\tint\n",
       "\tdepthT1395\tint\n",
       "\tdepthT1410\tint\n",
       "\tdepthT1425\tint\n",
       "\tdepthT1440\tint\n",
       "\tdepthT1455\tint\n",
       "\tdepthT1470\tint\n",
       "\tdepthT1485\tint\n",
       "\tdepthT1500\tint\n",
       "\tdepthT1515\tint\n",
       "\tdepthT1530\tint\n",
       "\tdepthT1545\tint\n",
       "\tdepthT1560\tint\n",
       "\tdepthT1575\tint\n",
       "\tdepthT1590\tint\n",
       "\tdepthT1605\tint\n",
       "\tdepthT1620\tint\n",
       "\tdepthT1635\tint\n",
       "\tdepthT1650\tint\n",
       "\tdepthT1665\tint\n",
       "\tdepthT1680\tint\n",
       "\tdepthT1695\tint\n",
       "\tdepthT1710\tint\n",
       "\tdepthT1725\tint\n",
       "\tdepthT1740\tint\n",
       "\tdepthT1755\tint\n",
       "\tdepthT1770\tint\n",
       "\tdepthT1785\tint\n",
       "\tdepthT1800\tint\n",
       "\tdepthT1815\tint\n",
       "\tdepthT1830\tint\n",
       "\tdepthT1845\tint\n",
       "\tdepthT1860\tint\n",
       "\tdepthT1875\tint\n",
       "\tdepthT1890\tint\n",
       "\tdepthT1905\tint\n",
       "\tdepthT1920\tint\n",
       "\tdepthT1935\tint\n",
       "\tdepthT1950\tint\n",
       "\tdepthT1965\tint\n",
       "\tdepthT1980\tint\n",
       "\tdepthT1995\tint\n",
       "\tdepthT2010\tint\n",
       "\tdepthT2025\tint\n",
       "\tdepthT2040\tint\n",
       "\tdepthT2055\tint\n",
       "\tdepthT2070\tint\n",
       "\tdepthT2085\tint\n",
       "\tdepthT2100\tint\n",
       "\tdepthT2115\tint\n",
       "\tdepthT2130\tint\n",
       "\tdepthT2145\tint\n",
       "\tdepthT2160\tint\n",
       "\tdepthT2175\tint\n",
       "\tdepthT2190\tint\n",
       "\tdepthT2205\tint\n",
       "\tdepthT2220\tint\n",
       "\tdepthT2235\tint\n",
       "\tdepthT2250\tint\n",
       "\tdepthT2265\tint\n",
       "\tdepthT2280\tint\n",
       "\tdepthT2295\tint\n",
       "\tdepthT2310\tint\n",
       "\tdepthT2325\tint\n",
       "\tdepthT2340\tint\n",
       "\tdepthT2355\tint\n",
       "\tdepthT2370\tint\n",
       "\tdepthT2385\tint\n",
       "\tdepthT2400\tint\n",
       "\tdepthT2415\tint\n",
       "\tdepthT2430\tint\n",
       "\tdepthT2445\tint\n",
       "\tdepthT2460\tint\n",
       "\tdepthT2475\tint\n",
       "\tdepthT2490\tint\n",
       "\tdepthT2505\tint\n",
       "\tdepthT2520\tint\n",
       "\tdepthT2535\tint\n",
       "\tdepthT2550\tint\n",
       "\tdepthT2565\tint\n",
       "\tdepthT2580\tint\n",
       "\tdepthT2595\tint\n",
       "\tdepthT2610\tint\n",
       "\tdepthT2625\tint\n",
       "\tdepthT2640\tint\n",
       "\tdepthT2655\tint\n",
       "\tdepthT2670\tint\n",
       "\tdepthT2685\tint\n",
       "\tdepthT2700\tint\n",
       "\tdepthT2715\tint\n",
       "\tdepthT2730\tint\n",
       "\tdepthT2745\tint\n",
       "\tdepthT2760\tint\n",
       "\tdepthT2775\tint\n",
       "\tdepthT2790\tint\n",
       "\tdepthT2805\tint\n",
       "\tdepthT2820\tint\n",
       "\tdepthT2835\tint\n",
       "\tdepthT2850\tint\n",
       "\tdepthT2865\tint\n",
       "\tdepthT2880\tint\n",
       "\tdepthT2895\tint\n",
       "\tdepthT2910\tint\n",
       "\tdepthT2925\tint\n",
       "\tdepthT2940\tint\n",
       "\tdepthT2955\tint\n",
       "\tdepthT2970\tint\n",
       "\tdepthT2985\tint\n",
       "\tdepthT3000\tint\n",
       "\tdepthT3015\tint\n",
       "\tdepthT3030\tint\n",
       "\tdepthT3045\tint\n",
       "\tdepthT3060\tint\n",
       "\tdepthT3075\tint\n",
       "\tdepthT3090\tint\n",
       "\tdepthT3105\tint\n",
       "\tdepthT3120\tint\n",
       "\tdepthT3135\tint\n",
       "\tdepthT3150\tint\n",
       "\tdepthT3165\tint\n",
       "\tdepthT3180\tint\n",
       "\tdepthT3195\tint\n",
       "\tdepthT3210\tint\n",
       "\tdepthT3225\tint\n",
       "\tdepthT3240\tint\n",
       "\tdepthT3255\tint\n",
       "\tdepthT3270\tint\n",
       "\tdepthT3285\tint\n",
       "\tdepthT3300\tint\n",
       "\tdepthT3315\tint\n",
       "\tdepthT3330\tint\n",
       "\tdepthT3345\tint\n",
       "\tdepthT3360\tint\n",
       "\tdepthT3375\tint\n",
       "\tdepthT3390\tint\n",
       "\tdepthT3405\tint\n",
       "\tdepthT3420\tint\n",
       "\tdepthT3435\tint\n",
       "\tdepthT3450\tint\n",
       "\tdepthT3465\tint\n",
       "\tdepthT3480\tint\n",
       "\tdepthT3495\tint\n",
       "\tdepthT3510\tint\n",
       "\tdepthT3525\tint\n",
       "\tdepthT3540\tint\n",
       "\tdepthT3555\tint\n",
       "\tdepthT3570\tint\n",
       "\tdepthT3585\tint\n",
       "\tdepthT3600\tint\n",
       "\tdepthT3615\tint\n",
       "\tdepthT3630\tint\n",
       "\tdepthT3645\tint\n",
       "\tdepthT3660\tint\n",
       "\tdepthT3675\tint\n",
       "\tdepthT3690\tint\n",
       "\tdepthT3705\tint\n",
       "\tdepthT3720\tint\n",
       "\tdepthT3735\tint\n",
       "\tdepthT3750\tint\n",
       "\tdepthT3765\tint\n",
       "\tdepthT3780\tint\n",
       "\tdepthT3795\tint\n",
       "\tdepthT3810\tint\n",
       "\tdepthT3825\tint\n",
       "\tdepthT3840\tint\n",
       "\tdepthT3855\tint\n",
       "\tdepthT3870\tint\n",
       "\tdepthT3885\tint\n",
       "\tdepthT3900\tint\n",
       "\tdepthT3915\tint\n",
       "\tdepthT3930\tint\n",
       "\tdepthT3945\tint\n",
       "\tdepthT3960\tint\n",
       "\tdepthT3975\tint\n",
       "\tdepthT3990\tint\n",
       "\tdepthT4005\tint\n",
       "\tdepthT4020\tint\n",
       "\tdepthT4035\tint\n",
       "\tdepthT4050\tint\n",
       "\tdepthT4065\tint\n",
       "\tdepthT4080\tint\n",
       "\tdepthT4095\tint\n",
       "\tdepthT4110\tint\n",
       "\tdepthT4125\tint\n",
       "\tdepthT4140\tint\n",
       "\tdepthT4155\tint\n",
       "\tdepthT4170\tint\n",
       "\tdepthT4185\tint\n",
       "\tdepthT4200\tint\n",
       "\tdepthT4215\tint\n",
       "\tdepthT4230\tint\n",
       "\tdepthT4245\tint\n",
       "\tdepthT4260\tint\n",
       "\tdepthT4275\tint\n",
       "\tdepthT4290\tint\n",
       "\tdepthT4305\tint\n",
       "\tdepthT4320\tint\n",
       "\tdepthT4335\tint\n",
       "\tdepthT4350\tint\n",
       "\tdepthT4365\tint\n",
       "\tdepthT4380\tint\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+------------+-------------+------------+-------------+------------+-----------+\n",
       "| tweet_user | out_degree2 | in_degree2 | all_degree2 | bi_degree2 | pagerank2 |\n",
       "+------------+-------------+------------+-------------+------------+-----------+\n",
       "|   114011   |      0      |    255     |     252     |     0      |  14.1023  |\n",
       "|   144652   |      7      |    1009    |     920     |     1      |  75.5127  |\n",
       "|   144652   |      7      |    1009    |     920     |     1      |  75.5127  |\n",
       "|   152768   |      0      |    547     |     543     |     0      |  77.5759  |\n",
       "|   155435   |      0      |    385     |     380     |     0      |  50.0258  |\n",
       "|   157223   |      0      |    1269    |     1096    |     0      |   82.633  |\n",
       "|   157223   |      0      |    1269    |     1096    |     0      |   82.633  |\n",
       "|   157223   |      0      |    1269    |     1096    |     0      |   82.633  |\n",
       "|   157245   |      0      |    2026    |     1989    |     0      |  218.638  |\n",
       "|   157245   |      0      |    2026    |     1989    |     0      |  218.638  |\n",
       "+------------+-------------+------------+-------------+------------+-----------+\n",
       "+------------------+---------------+-----------+-----+---------+---------+---------+\n",
       "|     weibo_id     |  release_time | wordlenth | URL | Hashtag | mention | picture |\n",
       "+------------------+---------------+-----------+-----+---------+---------+---------+\n",
       "| 3974606387442802 | 9.61666666667 |    392    |  1  |    1    |    0    |    1    |\n",
       "| 3957274441700193 | 13.7666666667 |     96    |  1  |    1    |    1    |    0    |\n",
       "| 3959404967835125 | 10.8666666667 |    173    |  0  |    1    |    0    |    0    |\n",
       "| 3954110896652586 | 20.2666666667 |    249    |  0  |    0    |    0    |    0    |\n",
       "| 3951263068328236 |     23.65     |     90    |  1  |    1    |    0    |    0    |\n",
       "| 3958367494594822 | 14.1666666667 |    162    |  0  |    0    |    0    |    0    |\n",
       "| 3963034382906252 | 11.2333333333 |     14    |  0  |    1    |    0    |    0    |\n",
       "| 3963022601009002 | 10.4666666667 |    296    |  0  |    1    |    0    |    0    |\n",
       "| 3966639445423588 |      10.0     |     86    |  0  |    0    |    1    |    0    |\n",
       "| 3956326495593412 |      23.0     |     67    |  0  |    0    |    0    |    0    |\n",
       "+------------------+---------------+-----------+-----+---------+---------+---------+\n",
       "+------------------+---------+---------+-----------+-----------+-------------+\n",
       "| content_category | hongbao | guanzhu | choujiang | pagerank1 | out_degree1 |\n",
       "+------------------+---------+---------+-----------+-----------+-------------+\n",
       "|        6         |    0    |    0    |     0     |  1.97947  |      0      |\n",
       "|        6         |    0    |    0    |     0     |  1.32334  |     141     |\n",
       "|        4         |    0    |    0    |     0     |  1.32334  |     141     |\n",
       "|        7         |    0    |    0    |     0     |  197.497  |      0      |\n",
       "|        9         |    0    |    0    |     0     |  10.1682  |      13     |\n",
       "|        6         |    0    |    0    |     0     |  0.176108 |      11     |\n",
       "|        1         |    0    |    0    |     0     |  0.176108 |      11     |\n",
       "|        6         |    0    |    0    |     0     |  0.176108 |      11     |\n",
       "|        7         |    0    |    1    |     0     |  36.1523  |      33     |\n",
       "|        0         |    0    |    0    |     0     |  36.1523  |      33     |\n",
       "+------------------+---------+---------+-----------+-----------+-------------+\n",
       "+------------+-----+\n",
       "| in_degree1 | ... |\n",
       "+------------+-----+\n",
       "|     64     | ... |\n",
       "|    279     | ... |\n",
       "|    279     | ... |\n",
       "|    2036    | ... |\n",
       "|    1358    | ... |\n",
       "|     8      | ... |\n",
       "|     8      | ... |\n",
       "|     8      | ... |\n",
       "|    9385    | ... |\n",
       "|    9385    | ... |\n",
       "+------------+-----+\n",
       "[10 rows x 718 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WeiboProfile_train_final=gl.SFrame.read_csv('WeiboProfile_train_final.csv')\n",
    "WeiboProfile_train_final.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/weibo_train_dataset/WeiboProfile_train_final.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/weibo_train_dataset/WeiboProfile_train_final.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 100 lines in 0.861929 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 100 lines in 0.861929 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Read 25027 lines. Lines per second: 14776.3</pre>"
      ],
      "text/plain": [
       "Read 25027 lines. Lines per second: 14776.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/weibo_train_dataset/WeiboProfile_train_final.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file /Users/xiaokeeie/all_datasets/weibojingsai/weibo_train_dataset/WeiboProfile_train_final.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 26955 lines in 1.7602 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 26955 lines in 1.7602 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[int,int,int,int,int,float,int,float,int,int,int,int,int,int,int,int,int,float,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#使用graphlab重新将数据载入到内存空间中\n",
    "WeiboProfile_train_final=gl.SFrame.read_csv('WeiboProfile_train_final.csv')\n",
    "\n",
    "column_names=[]\n",
    "range_scale= []\n",
    "range_scale.extend(range(75,72*61+1,15))\n",
    "\n",
    "for time_range in range_scale:\n",
    "    column_names.append('scaleT'+str(time_range))\n",
    "\n",
    "for item in column_names:\n",
    "    WeiboProfile_train_final[item]=WeiboProfile_train_final[item]-WeiboProfile_train_final['scaleT60']\n",
    "\n",
    "\n",
    "train_names=[]\n",
    "\n",
    "train_names1=[]\n",
    "train_scale=range(1,61,1)\n",
    "for time_range in train_scale:\n",
    "    train_names1.append('scaleT'+str(time_range))\n",
    "for time_range in train_scale:\n",
    "    train_names1.append('depthT'+str(time_range))\n",
    "train_names.extend(train_names1)\n",
    "\n",
    "#all_degree1-10\n",
    "\n",
    "train_names2=['out_degree1','in_degree1','all_degree1','bi_degree1','pagerank1']\n",
    "train_names.extend(train_names2)\n",
    "\n",
    "\n",
    "train_names3=['out_degree2','in_degree2','all_degree2','bi_degree2','pagerank2']\n",
    "train_names.extend(train_names3)\n",
    "\n",
    "train_names4=['release_time','wordlenth','URL','Hashtag','mention','picture','content_category','hongbao','guanzhu','choujiang']\n",
    "train_names.extend(train_names4)\n",
    "\n",
    "\n",
    "train_data, test_data = WeiboProfile_train_final.random_split(0.85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the train and test process for  scaleT75\n",
      "the train and test process for  scaleT135\n",
      "the train and test process for  scaleT195\n",
      "the train and test process for  scaleT255\n",
      "the train and test process for  scaleT315\n",
      "the train and test process for  scaleT375\n",
      "the train and test process for  scaleT435\n",
      "the train and test process for  scaleT495\n",
      "the train and test process for  scaleT555\n",
      "the train and test process for  scaleT615\n",
      "the train and test process for  scaleT675\n",
      "the train and test process for  scaleT735\n",
      "the train and test process for  scaleT795\n",
      "the train and test process for  scaleT855\n",
      "the train and test process for  scaleT915\n",
      "the train and test process for  scaleT975\n",
      "the train and test process for  scaleT1035\n",
      "the train and test process for  scaleT1095\n",
      "the train and test process for  scaleT1155\n",
      "the train and test process for  scaleT1215\n",
      "the train and test process for  scaleT1275\n",
      "the train and test process for  scaleT1335\n",
      "the train and test process for  scaleT1395\n",
      "the train and test process for  scaleT1455\n",
      "the train and test process for  scaleT1515\n",
      "the train and test process for  scaleT1575\n",
      "the train and test process for  scaleT1635\n",
      "the train and test process for  scaleT1695\n",
      "the train and test process for  scaleT1755\n",
      "the train and test process for  scaleT1815\n",
      "the train and test process for  scaleT1875\n",
      "the train and test process for  scaleT1935\n",
      "the train and test process for  scaleT1995\n",
      "the train and test process for  scaleT2055\n",
      "the train and test process for  scaleT2115\n",
      "the train and test process for  scaleT2175\n",
      "the train and test process for  scaleT2235\n",
      "the train and test process for  scaleT2295\n",
      "the train and test process for  scaleT2355\n",
      "the train and test process for  scaleT2415\n",
      "the train and test process for  scaleT2475\n",
      "the train and test process for  scaleT2535\n",
      "the train and test process for  scaleT2595\n",
      "the train and test process for  scaleT2655\n",
      "the train and test process for  scaleT2715\n",
      "the train and test process for  scaleT2775\n",
      "the train and test process for  scaleT2835\n",
      "the train and test process for  scaleT2895\n",
      "the train and test process for  scaleT2955\n",
      "the train and test process for  scaleT3015\n",
      "the train and test process for  scaleT3075\n",
      "the train and test process for  scaleT3135\n",
      "the train and test process for  scaleT3195\n",
      "the train and test process for  scaleT3255\n",
      "the train and test process for  scaleT3315\n",
      "the train and test process for  scaleT3375\n",
      "the train and test process for  scaleT3435\n",
      "the train and test process for  scaleT3495\n",
      "the train and test process for  scaleT3555\n",
      "the train and test process for  scaleT3615\n",
      "the train and test process for  scaleT3675\n",
      "the train and test process for  scaleT3735\n",
      "the train and test process for  scaleT3795\n",
      "the train and test process for  scaleT3855\n",
      "the train and test process for  scaleT3915\n",
      "the train and test process for  scaleT3975\n",
      "the train and test process for  scaleT4035\n",
      "the train and test process for  scaleT4095\n",
      "the train and test process for  scaleT4155\n",
      "the train and test process for  scaleT4215\n",
      "the train and test process for  scaleT4275\n",
      "the train and test process for  scaleT4335\n",
      "the value of rmse for scale preidction is  211.754380397\n"
     ]
    },
    {
     "data": {
      "image/png": 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AjC+6kkYwhM1p2Gm0oyNzzzgjnR/9aLL33q0uEQCA7aArKbBZDTuN1mrprSqh\nEACAYQRDmIBqt92m0ygAAFtMMISJ5MYbk1NPTcfPf67TKAAAW0wwhInglluS005LXvGK5JWvzJyb\nbtJpFACALab5DIwjwzqNvva16fzUp5KrrkrOOSd585uTnXba8F6dRgEAJhxdSSMY0p422mn0//7f\ndPb0JLvs0uoSAQAYJbqSQpvaaKfRlSuFQgAAtotgCOPB6tWp3XCDTqMAADSFYAhjWVUlX/1qcuSR\n6XjsMZ1GAQBoCsEQxqof/CB5wQuSnp7kE5/InO9/X6dRAACaQvMZaLFhnUbf8pZ0fuITyXe+k8yf\nn8yZk+yww4b36jQKANDWdCWNYMjEsdFOo2efnc7zz9dUBgCAhnQlhQlko51Gf/UroRAAgFEjGEIL\n1W6/XadRAABaTjCEVvjFL5LXvz4dt9+u0ygAAC0nGMJoWrs2ueii5Kijkj32yJxFi3QaBQCg5TSf\ngdFyzTXJ296W7LNPPRweeWQSnUYBANh6upJGMGTs22AExe67Z06tls6bb04+8pHkFa9Iyoj97zAA\nAG1opIPhjiP1IKCu4QiK3XfP3O9/P51HHdXq8gAAYBhnDGGENRxB8fDD6T3//FaWBQAAGyUYwkj6\n7W9T+973jKAAAGBcEQxhpNx8c3L88emo1YygAABgXBEMYXutWZPMn5/88R8nf/M3mXP11UZQAAAw\nruhKCttj8eLkrLOSvfZKPvWpZNq0JEZQAADQXMZVRDBk9G0wfmLq1Mw577x0Xnpp8sEPJv/wD8kb\n32gEBQAAo0YwjGDI6Go4fmKnnTL36KPT+YUvJIcc0uoSAQBoMyMdDJ0xhM1oOH7i8cfTe9hhQiEA\nABOCYAibUevvbzx+4v77W1EOAACMOMEQNqaqkv/5n3T8+MfGTwAAMKEJhvBkVZVcfnly3HFJT0/m\n/NM/GT8BAMCEpvkMbWtYp9G///t03nln0t2dPP540tOTvOxlSSnGTwAAMKboShrBkO3XsNPoU5+a\nuQcemM5//MfkFa9IOiyoAwAwNulKCiOgYafR3/42vSeckLzylUIhAABtxX/90pZq993XuNPoihWt\nKAcAAFpKMKT93HlnOn76U51GAQBgkGBI+6jVko99LDnppMyZOzfdhxyi0ygAAETzGdrFffclr3td\nsmpV8pnPJIcfrtMoAADjlq6kEQzZClWVfP7zyTvfmbz97ck55yQ77tjqqgAAYLuMdDD0X8hMKBvM\nJtxrr8z63zlWAAAgAElEQVQZGEjnPfck3/pWcuyxrS4PAADGJMGQCaPhbMLddsvcRYvS+YxntLo8\nAAAYszSfYcJoOJtw1ar0fuADrSwLAADGPMGQCaN2552NZxMuX96KcgAAYNwQDBn/arXkox9Nx623\nmk0IAADbQDBkfFuxInnJS5JLLsmcb3873TNmmE0IAABbybgKxq+vfz15wxuSN74xef/7kx13NJsQ\nAIC2YI5hBMO295vfJH/zN8lllyWf/Wxy8smtrggAAEaVOYa0nQ1mEz7taZlz113pPOaY5Cc/SXbf\nvdXlAQDAuGfFkDGt4WzCffapzyY85JBWlwcAAC0x0iuGms8wpjWcTfjAA+l9//tbWRYAAEwogiFj\nWu3nPzebEAAAmkwwZOy64op0/PjHZhMCAECTCYaMPVWVfPSjyVlnZU5vr9mEAADQZJrPMLY8/njy\n5jcnP/5xcumlSWen2YQAAPAk5hhGMJywli9PXv7y5KCDkv/6r2Tyk08XAgAAia6kTFQ33JA897nJ\nn/958sUvCoUAADCKDLinJTYYWv/b32bOHXek8+KLkz/7s1aXBgAAbUcwZNQ1HFp/4IGZ+8xnprPV\nxQEAQBuylZRR13Bo/b33prerq5VlAQBA2xIMGXW1O+80tB4AAMYQwZDRdckl6bj1VkPrAQBgDBEM\nGR1PPJH87d8mf/u3mfOVrxhaDwAAY8gm5xiWUv6wqqrvDr6fXlVV33o/e3lVVV8dhRob1WWO4Xjy\n0EPJq1+drF1bH0Wx996G1gMAwHYY1QH3pZQfVVV17JPfN/r7aBIMx5Fbb01e9rL660MfSnbUCBcA\nALbXSAfDzf1XetnI+0Z/hw1dckny1rcmF16YnHlmq6sBAAA2YnPBsNrI+0Z/p80NbQ+97750PPBA\n5jz8cDqvuCI55phWlwYAAGzC5oLhIaWUy1JfHVz3PoN/dyCMIQ2H1h98cObuvruh9QAAMMZt7ozh\nCzb14aqqrh7xiraAM4ZjT8/s2Zn3uc9tMJ9wIMkFZ56Z7s9+tlVlAQDAhDSqZwyfHPxKKZOSPDNJ\nf1VVvxipIhj/an19htYDAMA4tck5hqWUT5RSjhp8v1uSnyT5dJKbSymv3tzDSyn/UUpZWUq5Zb1r\n3aWU+0opPxp8vXi9n51bSrmrlHJ7KeVF2/yvYnQ9/HA6br/d0HoAABinNjfg/uSqqm4bfP/aJD+r\nquroJMcl+ZsteP5/JfnjBtc/UlXVsYOvbyVJKWVmktOTzEzykiT/VkrR+XSse/TR5CUvyZzTTjO0\nHgAAxqnNNZ9Zvd77U5N8KUmqqlqxJZmtqqrvl1Ia9R5p9OHTknyhqqq1SZaWUu5K8twkN2z2i2iN\nxx9PTjstOeqodH7yk5m7dGkuWG9o/VxD6wEAYFzYXDB8uJTyp0n6k5yU5PVJUkrZMcnO2/G9byul\nvCbJD5O8u6qqVUmmJrl+vXv6B68xFq1Zk7zylck++yT/7/8lpaRz+nSNZgAAYBza3FbSNyV5W+pb\nQv9vVVUrBq//UZKvb+N3/luSQ6qqek6SFUk+vI3PoVWeeCJ5zWvq7z/zmWSHHVpbDwAAsF0215X0\nZ0le3OD6t5N8e1u+sKqqB9b76yeT/O/g+/4kB673s2mD1xo677zzht7PmjUrs2bN2pZy2Fq1WvLX\nf5088EDy9a8nkya1uiIAAJjwFi5cmIULFzbt+ZubY/ivm/pwVVVv3+wXlHJwkv8dbFqTUsr+61Ye\nSynvTHJCVVVnlFKOTPK5JM9LfQvpgiSHNRpYaI5hi1RV8s53JjfemFxxRbLLLq2uCAAA2tKozjFM\n8uYkP01ySZLladw0ZqNKKZ9PMivJXqWUe5J0JzmllPKcJLUkS1PfrpqqqhaXUi5JsjjJmiRnS3+t\ntayvL71dXan196dj6tTM2WOPdH7/+8lVVwmFAAAwgWxuxXCvJK9M8ldJ1ib5YpIvV1X18OiUt9G6\nZMYmW9bXl4tOPTU9S5ZkcgbHT0yalLnXXZfO449vdXkAANDWRnrFcJPNZ6qq+mVVVZ+oquqU1OcY\n7p5k8WBHUSaw3q6uoVCYJJOT9KxZk94LL2xlWQAAQBNsbitpkqSUcmySV6c+y/CbSW5qZlG0Xq2/\nfygUrjM5SW358laUAwAANNEmg2Ep5e+TvDTJ7Um+kOTcwQH0THAdU6dmINkgHA4k6ZgypUUVAQAA\nzbK5M4a1JH1JHhu8tO7mkqSqqupZzS1vo3U5Y9hkyxYsyEUvfnF6arXfnTGcMSNzFyxI5/TprS4P\nAADa2kifMdxcMOzc1Ierqlo2UoVsDcGwye6/PznxxCw7++z0/uQnqS1fno4pUzJn/nyhEAAAxoBR\nDYabKKIjyaurqvrcSBWyld8vGDbLo48mL3hB8rKXJV1dra4GAABoYFS7kpZSdi2lnFtK+Vgp5UWl\nbm6Su5OcPlJFMEasXZucfnpy7LHJ3/1dq6sBAABGyea2kl6a5FdJrk/yR0n2Tf184TuqqvrxqFTY\nuC4rhiOtqpK//uvkvvuSyy5LJk1qdUUAAMBGjPSK4ebGVRxSVdXRg1/8qST3JzmoqqrHR6oAxoh/\n+IfkppuSq68WCgEAoM1sLhiuWfemqqonSin3CYUT0MUXJ//xH8l11yVPf3qrqwEAAEbZ5raSPpH6\npIKkvoV059RHV6wbV7Fr0ytsXJetpCNlwYJk9uxk4cJk5sxWVwMAAGyBUd1KWlXVDiP1RYwNy/r6\n0tvVlVp/fzp23jlzFi1K56WXCoUAANDGNreVlAlkWV9fLjr11PQsWfK7ofX77Ze506ZlkwMrAQCA\nCW2T4yqYWHq7uoZCYZJMTtKzcmV6zSsEAIC2Jhi2kVp//1AoXGdyktry5a0oBwAAGCMEwzbSMXXq\nUCehdQaSdEyZ0opyAACAMUIwbCNz3vKWdHd0DIXDgSTdM2Zkzvz5rSwLAABosU2OqxirjKvYBrVa\n8sIXZtmxx6Z3xYrUli9Px5QpmTN/fjqnT291dQAAwFYY6XEVgmG7+PCHk699Lbn66mQHU0gAAGA8\nEwwjGG61W25J/uiPkhtvTKwOAgDAuDfSwdAZw4nu8ceTM89MLrhAKAQAABqyYjjRvetdyb33Jpdc\nkpQR+38oAAAALTTSK4Y7jtSDGIOuvLIeCH/yE6EQAADYKFtJJ6qHHkpe+9rkP/8z2WuvVlcDAACM\nYbaSTkRVlfzVXyVTpiQXXtjqagAAgBFmKymb99nPJosXJxdf3OpKAACAccCK4USzdGlywgn184XP\nfnarqwEAAJrAiiHDLOvrS29XV2r33ZeOxYsz5/WvT6dQCAAAbCErhuPcsr6+XHTqqelZsiSTkwwk\n6T7kkMy98sp0mlsIAAATkgH3bKC3q2soFCbJ5CQ9d9+d3q6uVpYFAACMI4LhOFfr7x8KhetMTlJb\nvrwV5QAAAOOQYDjOdUydmoEnXRtI0jFlSivKAQAAxiFnDMe5ZUuW5KIjj0zP6tW/O2M4Y0bmLljg\njCEAAExQupKygc4bb8zcZzwjFzzzmandf386pkzJ3PnzhUIAAGCLWTEcz1avTo48MvnkJ5NTTml1\nNQAAwCjRlZTf+dSnkhkzhEIAAGC7WDEcrwYGksMOSy6/PDn22FZXAwAAjCIrhtT9y78kf/AHQiEA\nALDdrBiOR7/8ZXLEEcn119dXDQEAgLZixZDkgx9MXvlKoRAAABgRVgzHm3vvTZ7znOTWWxND7AEA\noC2N9IqhYDjevOENyT77JOef3+pKAACAFjHgvp3dcUdy6aXJXXe1uhIAAGACccZwPHnf+5L3vCfZ\nffdWVwIAAEwgtpKOFzfemLz85fXVwp13bnU1AABAC+lK2o6qKjnnnKS7WygEAABGnGA4HixYkPT3\nJ699basrAQAAJiDBcKyr1ZJzz03+4R+SHfUKAgAARp6kMUYt6+tLb1dXajffnI4VKzLn2GPT2eqi\nAACACUnzmTFoWV9fLjr11PQsWZLJSQaSdM+YkbkLFqRz+vRWlwcAALSY5jNtoLeraygUJsnkJD1L\nlqS3q6uVZQEAABOUYDgG1fr7h0LhOpOT1JYvb0U5AADABCcYjkEdU6dm4EnXBpJ0TJnSinIAAIAJ\nzhnDMWjZkiW5aObM9KxZ44whAAAwzEifMdSVdAzqvOWWzD3iiFzwrGeldv/96ZgyJXPnzxcKAQCA\nprBiONZUVfK85yXnnJO8/OWtrgYAABiDdCWd6L7zneSRR5KXvazVlQAAAG1CMBxrzj+/vlrY4X80\nAADA6JA+xpIbbkh+/vPkjDNaXQkAANBGBMOx5Pzzk/e8J5k0qdWVAAAAbUTzmbHittuSP/qjpK8v\n2XnnVlcDAACMYZrPTFQf/GDyjncIhQAAwKizYjgW9PUlxx+f3H13sttura4GAAAY46wYTkT//M/J\nm94kFAIAAC1hxbDVVqxIjjwyueOOZN99W10NAAAwDlgxnGguvDA580yhEAAAaBkrhq308MPJjBnJ\nj36UdHa2uhoAAGCcsGI4kXz848mf/ZlQCAAAtJQVw1Z57LFk+vRk4cJk5sxWVwMAAIwjVgwnik99\nKjnpJKEQAABoOSuGrbB6dXLooclXvpKccEKrqwEAAMaZkV4x3HGkHsTmLevrS29XV2o33ZSONWsy\nZ++943QhAADQalYMR8myvr5cdOqp6VmyJJOTDCTpnjEjcxcsSOf06a0uDwAAGEecMRyneru6hkJh\nkkxO0rNkSXq7ulpZFgAAgGA4Wmr9/UOhcJ3JSWrLl7eiHAAAgCFNDYallP8opawspdyy3rU9SilX\nlFLuLKV8u5Sy23o/O7eUclcp5fZSyouaWdto65g6NQNPujaQpGPKlFaUAwAAMKTZK4b/leSPn3Tt\nnCRXVlV1RJLvJjk3SUopRyY5PcnMJC9J8m+llBHbM9tqc+bPT/fuuw+Fw3VnDOfMn9/KsgAAAJrf\nfKaU0pnkf6uqetbg3+9I8oKqqlaWUvZPsrCqqmeUUs5JUlVV9aHB+76Z5Lyqqm5o8Mxx13wmtVqW\nTZ2a3mOPTe03v0nHlCmZM3++xjMAAMBWmwjjKvatqmplklRVtaKUsu/g9alJrl/vvv7BaxPD1Ven\nc7/90v31r7e6EgAAgA2MhTmG27T0d9555w29nzVrVmbNmjVC5TTJxRcnZ53V6ioAAIBxaOHChVm4\ncGHTnt+KraS3J5m13lbSq6qqmtlgK+m3knRPiK2kAwPJtGnJHXck++3X6moAAIBxbjzOMSyDr3Uu\nSzJn8P1ZSS5d7/qrSilPKaVMT3JokhtHob7m++pXk5NOEgoBAIAxqalbSUspn08yK8lepZR7knQn\n+WCSL5VSXpdkWeqdSFNV1eJSyiVJFidZk+Ts8bUsuAmf/nTyxje2ugoAAICGmr6VtBnG1VbS++5L\nnv3spL8/2WmnVlcDAABMAONxK2l7++xnk7/8S6EQAAAYswTDZqqq+jbS//N/Wl0JAADARgmGzfTD\nHyarVycnntjqSgAAADZKMGymdauFZcS2/gIAAIw4zWeaZfXqZOrU5MYbk+nTW10NAAAwgWg+M158\n4xvJkUcKhQAAwJgnGDaLpjMAAMA4YStpM/zyl8mMGcmyZcluu7W6GgAAYIKxlXQ8+MIXkj/5E6EQ\nAAAYFwTDZrCNFAAAGEcEw5F2xx3JvfcmL3xhqysBAADYIoLhSPv0p5Mzz0x23LHVlQAAAGwRzWdG\nUq2WdHbWR1UcfXSrqwEAACYozWfGsquuSvbZRygEAADGFcFwJGk6AwAAjEO2ko6URx9Npk1L7rwz\n2W+/VlcDAABMYLaSjlVf/Wpy8slCIQAAMO4IhiPFNlIAAGCcMlNhOy3r60vvu96V2tVXp2OffTLn\n+OPTOX16q8sCAADYYs4YbodlfX256NRT07NkSSYnGUjSPWNG5i5YIBwCAABN44zhGNLb1TUUCpNk\ncpKeJUvS29XVyrIAAAC2imC4HWr9/UOhcJ3JSWrLl7eiHAAAgG0iGG6HjqlTM/CkawNJOqZMaUU5\nAAAA28QZw+2wrK8vFx1zTHpWrXLGEAAAGDUjfcZQMNxOy447Lr177JFarZaOKVMyZ/58oRAAAGgq\nwTBjKBj+9rfJXnslK1cmk5982hAAAKA5dCUdS266KXnGM4RCAABgXBMMt8d11yUnntjqKgAAALaL\nYLg9rr1WMAQAAMY9Zwy3VVUl+++f/PCHyYEHtrYWAACgrThjOFbcfXfylKcIhQAAwLgnGG6ra69N\nTjqp1VUAAABsN8FwW2k8AwAATBCC4bbSeAYAAJggNJ/ZFg8/XD9b+NBDyaRJrasDAABoS5rPjAWL\nFiXHHy8UAgAAE4JguC2uu07jGQAAYMIQDLeFxjMAAMAE4ozh1lq7Ntlzz2Tp0vqfAAAAo8wZw1a7\n9dZ64xmhEAAAmCAEw61lTAUAADDBCIZbS+MZAABgghEMt5bGMwAAwAQjGG6N/v5kYCA57LBWVwIA\nADBiBMOtsW61sIxY8x8AAICWEwy3hsYzAADABCQYbg2NZwAAgAnIgPst9dhjyb77Jg88kOy88+h+\nNwAAwHoMuG+VH/wgOfpooRAAAJhwBMMtZUwFAAAwQQmGW0rjGQAAYIJyxnBL1GrJPvskP/1pcsAB\no/e9AAAADThj2Ao/+1my225CIQAAMCEJhlvi2muNqQAAACYswXBLaDwDAABMYILhltB4BgAAmMA0\nn9mcBx9MZsxIHnoo2WGH0flOAACATdB8ZrRdf33yvOcJhQAAwIQlGG7OdddpPAMAAExoguHmaDwD\nAABMcM4Ybsrq1cmeeybLlye77tr87wMAANgCzhiOph//ODn0UKEQAACY0ATDTTGmAgAAaAOC4aZo\nPAMAALQBwXBjqkrjGQAAoC0IhhuzbFlSqyUHH9zqSgAAAJpKMNyYddtIy4g1+gEAABiTdmx1AWPN\nsr6+9HZ1pXbVVenYf//M6etL5/TprS4LAACgacwxXM+yvr5cdOqp6VmyJJOTDCTpnjEjcxcsEA4B\nAIAxwxzDJurt6hoKhUkyOUnPkiXp7epqZVkAAABNJRiup9bfPxQK15mcpLZ8eSvKAQAAGBWC4Xo6\npk7NwJOuDSTpmDKlFeUAAACMCsFwPXPmz0/3jBlD4XDdGcM58+e3siwAAICm0nzmSZb19aV31qzU\nJk9Ox7HHZs78+RrPAAAAY8pIN58RDBs55pjk3/89OeGE5n0HAADANpowwbCUsjTJqiS1JGuqqnpu\nKWWPJF9M0plkaZLTq6pa1eCzzQuGtVry9Kcn99+f7Lprc74DAABgO0ykcRW1JLOqqjqmqqrnDl47\nJ8mVVVUdkeS7Sc4d9ar6++uBUCgEAADaRCuDYWnw/acluXjw/cVJXjaqFSXJnXcmRxwx6l8LAADQ\nKq0MhlWSBaWUH5RS3jB4bb+qqlYmSVVVK5LsO+pVCYYAAECb2bGF331SVVX3l1L2SXJFKeXO1MPi\n+jZ6kPC8884bej9r1qzMmjVrZKoSDAEAgDFm4cKFWbhwYdOePya6kpZSupM8muQNqZ87XFlK2T/J\nVVVVzWxwf/Oaz/zxHyfveEfyJ3/SnOcDAABspwnRfKaU8rRSyi6D7ycneVGSW5NclmTO4G1nJbl0\n1IuzYggAALSZlqwYllKmJ/la6ltFd0zyuaqqPlhK2TPJJUkOTLIs9XEVDzf4fHNWDH/zm2TPPZNH\nH0122GHknw8AADACRnrFsCVnDKuq6kvynAbXH0rywtGvaNBddyWHHCIUAgAAbaWVXUnHHttIAQCA\nNiQYrk8wBAAA2pBguD7BEAAAaEOC4foEQwAAoA2NiTmGW6spXUmrKtl996Svr96ZFAAAYIyaEHMM\nx6SVK5OnPEUoBAAA2o5guI5tpAAAQJsSDNcRDAEAgDYlGK4jGAIAAG1KMFxHMAQAANqUYLiOYAgA\nALQp4yqSZPXqZNddk1//ut6ZFAAAYAwzrqIZlixJDjpIKAQAANqSYJgkd9xhGykAANC2BMPE+UIA\nAKCtCYaJYAgAALQ1wTARDAEAgLYmGCaCIQAA0NYEwwcfTJ54Itl331ZXAgAA0BKC4brVwjJiI0AA\nAADGFcHQNlIAAKDNCYaCIQAA0OYEQ8EQAABoc4KhYAgAALS5UlVVq2vYaqWUakTqXrs22WWX5Fe/\nSnbeefufBwAAMApKKamqasQ6aLb3imFfX3LAAUIhAADQ1to7GNpGCgAAIBgKhgAAQLsTDAVDAACg\nzQmGgiEAANDmBEPBEAAAaHPtGwxXrUoGBpKpU1tdCQAAQEu1bzC8887k8MOTMmKjPwAAAMal9g6G\ntpECAAAIhgAAAO1OMAQAAGhzgiEAAECbK1VVtbqGrVZKqbar7lot2WWX5IEHksmTR64wAACAUVBK\nSVVVI9ZJsz1XDO+5J9lrL6EQAAAg7RoMbSMFAAAY0p7B8I47BEMAAIBB7RkMrRgCAAAMEQwBAADa\nnGAIAADQ5tpvXMWjjyb77lv/s6M9czEAADC+GVexvX72s+TQQ4VCAACAQe2XjmwjBQAA2IBgCAAA\n0OYEQ/5/e/ceJFlZ3nH8+1tucguiicgl3LEQUrhZhWCISTCCxBgxGgFjgpSJJgqYQEUxEoJmNYGU\nFlIgVhQUMFEUU0Q0RJdENyTBBVQui1zkshBYLsHSKKJSwD75o99xD8P0sjM73T0z/f1Udc3bb58+\n5z1PPzPTT5/3nJYkSZI05iwMJUmSJGnMjddVSatg661h9WrYZpvZH5gkSZIkDYFXJd0Qq1fDVltZ\nFEqSJElSx3gVhk4jlSRJkqSnsDCUJEmSpDFnYShJkiRJY87CUJIkSZLG3MajHsAw3L1qFeefcgpr\nli9n0aabcsy++7LLbruNeliSJEmSNCcs+K+ruHvVKs465BDee8cdbAk8Apy6xx4cf/nlFoeSJEmS\n5iW/rmKazj/llJ8WhQBbAu+94w7OP+WUUQ5LkiRJkuaMBV8Yrlm9+qdF4YQtgTX33TeK4UiSJEnS\nnLPgC8NFO+7II5P6HgEW7bDDKIYjSZIkSXPOgi8Mj1m6lFP32OOnxeHEOYbHLF06ymFJkiRJ0pyx\n4C8+A52rkt53H4t22IFjli71wjOSJEmS5q3ZvvjMWBSGkiRJkrSQeFVSSZIkSdKssjCUJEmSpDFn\nYShJkiRJY87CUJIkSZLGnIWhJEmSJI05C0NJkiRJGnMWhpIkSZI05iwMJUmSJGnMWRhKkiRJ0piz\nMJQkSZKkMTcnC8MkhyW5Jcm3k5w06vFIkiRJ0kI25wrDJIuAs4GXA/sCr0+y92hHpeXLl496CGPH\nmA+fMR8+Yz58xnz4jPnwGfPhM+bz35wrDIEDgNuq6u6qegy4CDh8xGMae/6yD58xHz5jPnzGfPiM\n+fAZ8+Ez5sNnzOe/uVgY7gjc07l/b+uTJEmSJA3AXCwMJUmSJElDlKoa9RieJMmBwHuq6rB2/11A\nVdXpnWXm1qAlSZIkaciqKrO1rrlYGG4E3Ar8BnA/cDXw+qq6eaQDkyRJkqQFauNRD2CyqnoiyXHA\nMnpTXc+zKJQkSZKkwZlzRwwlSZIkScM17y4+k+SwJLck+XaSk0Y9noUkyV1Jrk9ybZKrW9+2SZYl\nuTXJl5Ns01n+L5LcluTmJIeObuTzR5LzkjyY5IZO37RjnGRJkhva78GHhr0f80mfmJ+a5N4k32y3\nwzqPGfMNkGSnJF9J8q0kK5O8vfWb5wMyRcyPb/3m+YAk2SzJVe3/5cokp7Z+83xA1hFz83zAkixq\nsb203TfPB6zF/NpOzIeT51U1b270CtnbgV2ATYDrgL1HPa6FcgPuBLad1Hc68M7WPgk4rbX3Aa6l\nNx151/a6ZNT7MNdvwK8Ai4EbNiTGwFXA/q19GfDyUe/bXL31ifmpwIlTLPt8Y77B8X4usLi1t6J3\nzvje5vlIYm6eDzbuW7SfGwEr6H0Ps3k+/Jib54OP+wnAPwCXtvvm+fBjPpQ8n29HDA8Abququ6vq\nMeAi4PARj2khCU89inw4cEFrXwC8urVfBVxUVY9X1V3AbfReH61DVf0X8L1J3dOKcZLnAltX1TVt\nuQs7z9EkfWIOvXyf7HCM+Qapqgeq6rrW/iFwM7AT5vnA9In5xPf/mucDUlU/as3N6L0pK8zzgeoT\nczDPBybJTsArgHM73eb5APWJOQwhz+dbYbgjcE/n/r2s/eenDVfA5UmuSfJHrW+7qnoQem8+gOe0\n/smvxWp8LWbqOdOM8Y70cn+Cvwczc1yS65Kc25kGY8xnUZJd6R2tXcH0/5YY8xnoxPyq1mWeD8jE\nVC/gAeDy9gbMPB+gPjEH83yQzgDewdoiHMzzQZsq5jCEPJ9vhaEG66CqWkLvU4pjk7yEpyalVysa\nPGM8eOcAu1fVYnpvMD444vEsOEm2Aj4H/Gk7iuXfkgGbIubm+QBV1Zqq+kV6R8QPSLIv5vlATRHz\nfTDPBybJbwEPthkJ6/quPPN8lqwj5kPJ8/lWGK4Gdu7c36n1aRZU1f3t50PAP9ObGvpgku0A2mHp\n/22LrwZ+vvN0X4uZm26Mjf0GqqqHqk26Bz7G2mnQxnwWJNmYXoHyyar6fOs2zwdoqpib58NRVT8A\nlgOHYZ4PRTfm5vlAHQS8KsmdwKeBlyb5JPCAeT4wU8X8wmHl+XwrDK8B9kyyS5JNgaOAS0c8pgUh\nyRbt02aSbAkcCqykF99j2mJvBCbe5F0KHJVk0yS7AXsCVw910PNXePKnQNOKcZu28f0kByQJcHTn\nOZrak2Le/pFNeA1wY2sb89nxceCmqjqz02eeD9ZTYm6eD06Sn52YypVkc+AQeud2mucD0ifmt5jn\ng1NV766qnatqd3rvub9SVX8AfAHzfCD6xPzoYeX5nPuC+3WpqieSHAcso1fUnldVN494WAvFdsAl\nSYpeXvxjVS1L8nXgs0neBNwNHAFQVTcl+SxwE/AY8LbOJxnqI8mngF8Hnp3kf+hdZeo04OJpxvhY\n4MLED3YAAAX6SURBVHzgGcBlVfWlYe7HfNIn5gcnWQysAe4C/hiM+WxIchDwBmBlOxeogHfTu4rd\ndP+WGPP1sI6Y/555PjDbAxckWUTv/chnquqyJCswzwelX8wvNM+H7jTM82H7u2HkuV9wL0mSJElj\nbr5NJZUkSZIkzTILQ0mSJEkacxaGkiRJkjTmLAwlSZIkacxZGEqSJEnSmLMwlCRJkqQxZ2EoSZqx\nJCcnuTHJ9Um+mWT/IW//E0le09ofS7L3LKxzmyRv7dzfvn1P1KxLcniSv5zmc76Y5GdmuL0XJPnN\nGT53kyT/0b5HTpK0wPjHXZI0I0kOBF4BLK6qFwAvA+6ZxvM3ms3xVNWbq+qWWVjVtsDbOuu9v6qO\nmIX1TuWdwDnTeUJVvbKqfjDD7S2m95pNW1U9BvwbcNQMty1JmsMsDCVJM7U98J2qehygqr5bVQ8A\nJFmV5PQkNyRZkWT31v+JJB9JsgI4PckWSc5ry3wjyW+35XZJckWSr7fbgRMbTXJ2kpuTLAOe0+n/\napIlrf1wkvcluS7JlUl+rvXvnuRr7Qjn0iQPT7Fffwvs3o6Ant7GsrI9/41JLkmyLMmdSY5NckJb\n9sokz+xs51+TXNOOsj1v8kaS7AX8pKq+24nNOW18tyf5tRabm5J8vPO8VUme1cZ1U5KPtqO2X0qy\n2RSxeHZ7zsbAXwNHtPG+bh3x3yfJVW2565Ls0Tb/eeAN00kSSdL8YGEoSZqpZcDOSW5J8uEkvzrp\n8e9V1X7Ah4EzO/07VtWBVfXnwMnAv1fVgcBLgQ8k2Rx4EHhZVb2I3hGqswDatNG9qur5wBuBX+4z\nti2BK6tqMfCfwJtb/5nAGe0I571ATfHcdwF3VNWSqjqp9XWX2xd4NXAA8H7gh1W1BFgBHN2W+Shw\nXFXtD7wD+MgU2zkI+OakvmdW1YuBE4FLgQ9W1T7Afkn2m2IsewJnVdUvAN8HXjtlNKBaAf9XwGfa\nvl1M//j/CfChtl8vohcrgBuBoU4XliQNh4WhJGlGquoRYAnwFuAh4KIkR3cWuaj9/DRwYKf/4k77\nUOBdSa4FlgObAju3n+cmuaEt//y2/Eva+qiq+4Gv9Bneo1V1WWt/A9i1tV8MfK61P7U++zmFr1bV\nj6rqO8D/AV9s/SuBXZNsSa9gvbjt198D202xnu3pxa3rC511PVBVN7X73+rsQzrLr6qqla3d3c/1\n1S/+XwNOTvIOYNeqehSgqtYAj7Z9lCQtIBuPegCSpPmrqgq4AriiTbc8Grhw4uHuop32I5NW89qq\nuq3bkeRUeoXRfu1cxB9Pc2iPddpPsPb/XXcc3QJrOh7ttKtzf03bziJ6R0uXPM16fgxMvohMd13d\n7Uyse11jeQJ4Rms/ztoPf5/Buj0l/sCtbbrvK4HLkrylqpa3xzYDfvI065QkzTMeMZQkzUiS5yXZ\ns9O1GLi7c//I9vMoekegpvJl4O2ddS5uzW2A+1v7aGDiQjVXAEcmWZRke+DgfsPr078C+N3OuKby\nMLB1n8eeVlU9DKxKMrEdOtNAu24G9lrHqtancO23zF30poACvK7T/zBPLkanjH+S3apqVVWdRe+8\nwv1a/7PonVf6xHqMTZI0j1gYSpJmaivggnbhk+voTfd8T+fxbZNcDxwP/Fnrm3xO3/uATdpFalbS\nuzgK9K7UeUyb4vg82lHGqroEuJ3e1MrzgSs76+p3hLLrBODENt496J2X9yTtYjD/3cZ0ep/1PN12\nfh/4w3bhlhuBV02xzBX0iul+6+q3P+uznx8A3prkG8CzOv1fBfaZuPgMsJS18b+RtfE/or2u19I7\np3LiKPDBwL/02aYkaR5LbxaQJEmzJ8kq4IUTV9ycK5JsXlU/bu0jgaOq6ndGOJ4zgC9UVb9zJeeU\nJP8EnFRVt496LJKk2eU5hpKkQZirnzq+MMnZ9KZgfg9404jH8zfAL414DOslySbAJRaFkrQwecRQ\nkiRJksac5xhKkiRJ0pizMJQkSZKkMWdhKEmSJEljzsJQkiRJksachaEkSZIkjTkLQ0mSJEkac/8P\nI0O3TmtlKMUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116930f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test_data=WeiboProfile_train_final\n",
    "test_names=[]\n",
    "\n",
    "#验证全部时间范围内的结果\n",
    "result_tmp=[]\n",
    "test_scale=range(75,72*61+1,60)\n",
    "\n",
    "#test_scale=range(75,91,15)\n",
    "for time_range in test_scale:\n",
    "    test_names.append('scaleT'+str(time_range))\n",
    "rmse_scale_results=[]\n",
    "for test_column in test_names:\n",
    "    regression_model = gl.random_forest_regression.create(train_data, target=test_column, features = train_names1, verbose=False)\n",
    "    print \"the train and test process for \",test_column\n",
    "    predictions = regression_model.predict(test_data)\n",
    "    result_tmp.append(predictions)\n",
    "    results = regression_model.evaluate(test_data)\n",
    "    rmse_scale_results.append(results['rmse'])\n",
    "\n",
    "print \"the value of rmse for scale preidction is \",np.mean(rmse_scale_results)\n",
    "\n",
    "fig = gcf() \n",
    "fig.set_size_inches(15, 9)\n",
    "\n",
    "plot(test_scale,rmse_scale_results,'ro-')\n",
    "xlabel('Spreading time (minutes)')\n",
    "ylabel('RMSE')\n",
    "\n",
    "savefig(\"testset_scale_result.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the train and test process for  depthT75\n",
      "the train and test process for  depthT135\n",
      "the train and test process for  depthT195\n",
      "the train and test process for  depthT255\n",
      "the train and test process for  depthT315\n",
      "the train and test process for  depthT375\n",
      "the train and test process for  depthT435\n",
      "the train and test process for  depthT495\n",
      "the train and test process for  depthT555\n",
      "the train and test process for  depthT615\n",
      "the train and test process for  depthT675\n",
      "the train and test process for  depthT735\n",
      "the train and test process for  depthT795\n",
      "the train and test process for  depthT855\n",
      "the train and test process for  depthT915\n",
      "the train and test process for  depthT975\n",
      "the train and test process for  depthT1035\n",
      "the train and test process for  depthT1095\n",
      "the train and test process for  depthT1155\n",
      "the train and test process for  depthT1215\n",
      "the train and test process for  depthT1275\n",
      "the train and test process for  depthT1335\n",
      "the train and test process for  depthT1395\n",
      "the train and test process for  depthT1455\n",
      "the train and test process for  depthT1515\n",
      "the train and test process for  depthT1575\n",
      "the train and test process for  depthT1635\n",
      "the train and test process for  depthT1695\n",
      "the train and test process for  depthT1755\n",
      "the train and test process for  depthT1815\n",
      "the train and test process for  depthT1875\n",
      "the train and test process for  depthT1935\n",
      "the train and test process for  depthT1995\n",
      "the train and test process for  depthT2055\n",
      "the train and test process for  depthT2115\n",
      "the train and test process for  depthT2175\n",
      "the train and test process for  depthT2235\n",
      "the train and test process for  depthT2295\n",
      "the train and test process for  depthT2355\n",
      "the train and test process for  depthT2415\n",
      "the train and test process for  depthT2475\n",
      "the train and test process for  depthT2535\n",
      "the train and test process for  depthT2595\n",
      "the train and test process for  depthT2655\n",
      "the train and test process for  depthT2715\n",
      "the train and test process for  depthT2775\n",
      "the train and test process for  depthT2835\n",
      "the train and test process for  depthT2895\n",
      "the train and test process for  depthT2955\n",
      "the train and test process for  depthT3015\n",
      "the train and test process for  depthT3075\n",
      "the train and test process for  depthT3135\n",
      "the train and test process for  depthT3195\n",
      "the train and test process for  depthT3255\n",
      "the train and test process for  depthT3315\n",
      "the train and test process for  depthT3375\n",
      "the train and test process for  depthT3435\n",
      "the train and test process for  depthT3495\n",
      "the train and test process for  depthT3555\n",
      "the train and test process for  depthT3615\n",
      "the train and test process for  depthT3675\n",
      "the train and test process for  depthT3735\n",
      "the train and test process for  depthT3795\n",
      "the train and test process for  depthT3855\n",
      "the train and test process for  depthT3915\n",
      "the train and test process for  depthT3975\n",
      "the train and test process for  depthT4035\n",
      "the train and test process for  depthT4095\n",
      "the train and test process for  depthT4155\n",
      "the train and test process for  depthT4215\n",
      "the train and test process for  depthT4275\n",
      "the train and test process for  depthT4335\n",
      "the value of rmse for scale preidction is  [0.396796314897023, 0.7733150886026379, 0.9746725414627491, 1.09906945962153, 1.180973962362181, 1.2448057660187504, 1.2815792724791506, 1.3246351395510307, 1.3625018315281736, 1.4004196691018487, 1.4355520847560561, 1.4584315901076252, 1.4863493904480256, 1.5143889561544306, 1.5226791316220154, 1.5337018021152478, 1.5534417632997566, 1.5806222981005464, 1.5945323714281943, 1.608474011065226, 1.6193448720808492, 1.6270004364098343, 1.6319911518341563, 1.6384052676238774, 1.645722974965286, 1.6492652631354519, 1.6569553330579985, 1.6631335569281003, 1.6611322956164722, 1.666237141200782, 1.6699839515975459, 1.6683337367708428, 1.6755251369317445, 1.6748517863026995, 1.6809515966309179, 1.6824189100141147, 1.685404468375076, 1.6859338472357583, 1.6838691062018767, 1.6925261921765862, 1.697072016894738, 1.7012775054983584, 1.7038212612950479, 1.7060859874618441, 1.7055363019490957, 1.7059829635380088, 1.707939621555197, 1.707871373004585, 1.7082242516689863, 1.7096967000300658, 1.712684075060528, 1.7063366266135755, 1.707154877968815, 1.70749227212617, 1.7075486708318115, 1.7101110627501799, 1.7123695442651465, 1.7072307995533575, 1.71146006717022, 1.7119294650282006, 1.7110515449264736, 1.7110352927366446, 1.7157540969074296, 1.7215511960134118, 1.7193095874903588, 1.722260524721766, 1.7272183850465725, 1.7232597485108352, 1.7275404966505574, 1.7286548137194988, 1.727775499490926, 1.729750705785529]\n"
     ]
    },
    {
     "data": {
      "image/png": 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AMOyEQWAkbO3G7J2cdVazIMuxxzbHS1+avPvdzQqcm9p9963bPgEAYNQIg8BI2NyzfXfe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\nYERERERExAT0P+Gp4lPzjQu7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115302110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_names=[]\n",
    "\n",
    "#验证全部时间范围内的结果\n",
    "result_tmp=[]\n",
    "\n",
    "test_scale=range(75,72*61+1,60)\n",
    "#test_scale=range(75,72*61+1,300)\n",
    "for time_range in test_scale:\n",
    "    test_names.append('depthT'+str(time_range))\n",
    "    \n",
    "rmse_depth_results=[]\n",
    "for test_column in test_names:\n",
    "    regression_model = gl.random_forest_regression.create(train_data, target=test_column, features = train_names1, verbose=False)\n",
    "    print \"the train and test process for \",test_column\n",
    "    predictions = regression_model.predict(test_data)\n",
    "    results = regression_model.evaluate(test_data)\n",
    "    rmse_depth_results.append(results['rmse'])\n",
    "\n",
    "print \"the value of rmse for scale preidction is \",rmse_depth_results\n",
    "\n",
    "\n",
    "fig = gcf() \n",
    "fig.set_size_inches(15, 9)\n",
    "plot(test_scale,rmse_depth_results,'bs-')\n",
    "xlabel('Spreading time (minutes)')\n",
    "ylabel('RMSE')\n",
    "savefig(\"testset_depth_result.pdf\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 基于Xgboost的机器学习框架"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/sklearn/preprocessing/label.py:112: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "//anaconda/lib/python2.7/site-packages/sklearn/preprocessing/label.py:147: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12204d550>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pA7YHHjY0Myve9vhKlA0R8b9Ah/wDbKeTfQ6hmZlZm2tt8Xorv2vvL8Ddkm6m\n4Esprf1IZcw7lZzgrKWSStZUckJaWVtbvE4m+zSMS4EpZLfNn1SqUGZmZi1p1ZwXgKQeZJ8d+Kf8\n60d2iIhVJU3XTnjOy8yseO97zkvSWOA3ZN99BbAv8LvtE8/MzKw4rR02vBD4JLASICJeBD5YqlBW\nPqmMeaeSE5y1VFLJmkpOSCtra4vX+oh4p76Rf+K6x8HMzKwsWvs+r+uAt4BzyL5y5AJgfkR8rbTx\n2gfPeZmZFW+bP9uw4AAdgM+TfVuxyL7K5Gf+jdw6Ll5mZsXb5hs26j+/MCI2R8StEfHpiDg9X/dv\n43YolTHvVHKCs5ZKKllTyQlpZd3anFfDHYWSflviLGZmZq2yta9EeToiBm65bsXxsKGZWfHez/u8\nopl1MzOzstla8TpY0kpJq4CD8vWVklZJWtkWAa1tpTLmnUpOcNZSSSVrKjkhraxVLT0YETu0VRAz\nM7PWavVnG9q285yXmVnxtsf3eZmZmVUMFy9rJJUx71RygrOWSipZU8kJaWV18TIzs+R4zqsNeM7L\nzKx4nvMyM7N2xcXLGkllzDuVnOCspZJK1lRyQlpZXbzMzCw5ZSteknpImttE/08l9W3lMUZL+v72\nT9c6kk6XNE/SJkmDypVje6qpqSl3hFZJJSekmXXRokUce+yxHHjggQwYMIDvfe97ACxfvpxhw4bR\np08fhg8fzooVKwB48803OfbYY9ltt924+OKLGx3zyiuvpHv37nTu3LkkWStdKjkhraxlu2FDUg/g\n9xFx0Ps4xmjgkIi4eKsbb/1YRd9VIakPsBn4CfCViJjVzHa+W8OS0bVrD+rqZrBkyRKqq6tZvXo1\nhxxyCJMmTWLChAnstddefPWrX+Xaa69l+fLlfPvb32bt2rXU1dUxb9485s2b11DsAJ544gl69OhB\n7969WbnSnypnrVfJN2zsKOkuSfMl/VrSLpKmt3QVI+k8Sc9LmgF8sqB/b0m/kfR4vhxZ0P+wpLmS\nbpW0UNKe+ZXfc5LuyK8APyLpeEl/k/SkpHskdcyPMUhSraSZkh6U1BUgIp6PiBfJvqBzKyKRZXoF\nZGhPOdPLunTpy3Tr1o3q6moAOnXqxAEHHMCiRYuYNGkSo0ePBmD06NH87nfZtyZ17NiRI488kp13\n3pktHX744XTt2vU9/e9XKvMzqeSEtLKWu3j1AX4QEf2AlcAFZP8HNUlSN+Aq4BPAUUC/godvBm6M\niMHA6cBaktaSAAAQlklEQVTP8v7xwCMRMQD4DbBfwT4fy59/ALAWuBL4p4g4FHgK+JKkKuD7wGkR\ncRgwAbjm/bxos5QsXLiQuro6jjjiCJYuXdpQiLp168ayZcvKnM7+UbX4wbxt4JWImJGv3w1sbfhv\nMDA9It4EkHQP0Dt/7DjgAEn1V0GdJO1KVuROAYiIhyQtLzjeyxExM18/gqwYPpYfY0fgv8gKbH9g\nat7fAXit+Jd6LtAzX98DqAZq8nZt/m8ltGsqLE9LbbbyeKW06/sqJU9L7ayvtraWmpoaVq9ezfDh\nwzn//PPp1KkTkhr+Oq+pqXlPG7L5svr9649VaMvt23u7vq9S8rTUrqmpKevz19bWMnHiRAB69uxJ\niyKiLAvQA1hQ0B4K3AdMAwY1s8/JwB0F7YuA7+Xry4Adm9hnFtCjoP2/wJ75888p6P9n4O4m9u8P\nPLaV1zK9ucz54wHhxUsiCxERsWHDhhg+fHjcdNNNUa9v376xZMmSiIh4/fXXo2/fvlFo4sSJcdFF\nF0VTdttttyb7zZqT/7dIU0u5hw17SBqcr58N/IWW548eB46R1EXSjsCnCx57GLikviHp4Hz1MeCM\nvG8Y2WVPw2YF6zOAT0r6aL5tR0m9geeBfSQdkfdXSSocrmzqWAmrLXeAVqotd4Ai1JY7QBFqG9bG\njBlDv379uOSShv+tGDVqVMNfxnfccQcnn3zye46Q/c55r+b6tzlpIvMzqeSEtLKWu3g9B1woaT6w\nO/AjaH7OKyKWkM15zSArdPMLHr4EOFTSbEnzgH/N+68Gjpc0BzgNWAKsqj9kwbHfIBvb+6Wk2cDf\ngD4RsYFsDu1aSXXA02Rzbkg6RdKrZEOOf5D04DaeB7OK8thjj3H33Xczbdo0Bg4cyKBBg5gyZQrj\nxo1j6tSp9OnTh0ceeYTLL7+8YZ/999+fL3/5y9xxxx10796d5557DoBx48ax3377sW7dOrp3787V\nV19drpdl7Ui7/2xDSTsBmyJiU3719MOIaNP3ZPlWeUtJ1649WLJkYbljmLV4q3y5b9hoC92BX0vq\nAKwHxpYjRHv/I8HMrC2Ve9iwWZJmSJqVL0/n/x5Y7HEi4r8jYlBEVEfE4Ih4qhR524tUxrxTyQnO\nWiqpZE0lJ6SVtWKvvCLiiHJnMDOzytTu57wqgb/Py8yseJX88VBmZmZFc/GyRlIZ804lJzhrqaSS\nNZWckFZWFy8zM0uO57zagOe8zMyK5zkvMzNrV1y8rJFUxrxTyQnOWiqpZE0lJ6SV1cXLzMyS4zmv\nNuA5LzOz4nnOy8zM2hUXL2sklTHvVHKCs5ZKKllTyQlpZXXxMjOz5HjOqw14zsvMrHie8zIzs3bF\nxcsaSWXMO5Wc4KylkkrWVHJCWlldvMzMLDme82oDnvMyMyue57zMzKxdcfGyRlIZ804lJzhrqaSS\nNZWckFZWFy8zM0uO57zagOe8zMyK5zkvMzNrV1y8rJFUxrxTyQnOWiqpZE0lJ6SV1cXLzMyS4zmv\nNuA5LzOz4nnOy4qyefNmBg4cyKhRowCYPXs2Rx55JAcffDAnn3wyq1evbtj2W9/6Fr179+aAAw7g\n4YcfLldkM/sHUxHFS9ICSXvm66uK2G+CpFNLl2yrz///JL0iaWUrtq34pVu3ntTW1nLzzTdz4IEH\nNmQfO3Ys1113HbNnz+ZTn/oU1113HQDz58/n17/+Nc8++ywPPvggF1xwAW11hZnS2LyzlkYqWVPJ\nCWllrYjiBUQz621C0g7buOtk4LDWbRoVvyxd+jJ///vfeeCBBzj//PMbkr/wwgscddRRABx33HH8\n9re/zV785MmceeaZVFVV0bNnT3r37s0TTzzR2nNnZrbN2rx4Sbpf0kxJcyXV/4Zsckyzmf1/IOlZ\nSQ8DHyzoHySpNj/2g5K65v2HSZotaZak6yTNzftHS5ok6RHgT3nfVyQ9IalO0viCY/+LpMfzY/xI\nkgAi4omIWPq+T0oF+fWvf831119P/hIB6N+/P5MnT254fNGiRQAsXryY/fbbr2G7fffdl8WLF7dJ\nzpqamjZ5nu3BWUsjlayp5IS0spbjyuu8iDiM7IrlkvrhwtaQ9Cmgd0QcAIwGjsz7q4DvA6flx54A\nXJPvdjswNiIGAZtofGU3EDg1IoZKOj4/9uF5/6GSjpLUFzgDODI/xmbgX7b1xVe6rl27Ul1d3Wj4\n77bbbuOWW27hsMMOY82aNey0005lTGhmBlVleM5LJZ2Sr38E6F3EvscAvwSIiNclTcv7+wD9gan5\nVVEH4DVJuwOdIqJ+LOsXwIkFx5saESvy9WHA8ZJmkV0J7ppnOxg4BJiZH/sDwDZcbZ0L9MzX9wCq\ngZq8XZv/W+423Hvvvdx3332sX7+e9evXc8455zBmzBiuuOIKampqePHFF/nlL39JbW0t++67L6++\n+mrDOPmiRYvYd999G9r1f8WVol1XV8ell15asuNvz/ZNN91EdXV1xeRpqV0451EJeVpqb5m53Hn8\n898+zz9x4kQAevbsSYsios0WYAjwZ2DnvD0973sJ2DPvW9nC/t8Fzi1o/xY4laxwPdbE9rsDCwra\nA4A5+fpo4HsFj32H7Apty2P8H+CbW3ldzWbOHw+IBBZi+vTpERFRW1sbJ510UkRELFu2LCIiNm3a\nFOecc05MmDAhIiKeeeaZqK6ujvXr18dLL70UH/3oR2Pz5s3RFupzpsBZSyOVrKnkjKi8rFmJavr3\nalsPG+4OLI+I9flw3BF5f+GcV0vzX38GzpDUQdKHgKF5//PAPpKOgGwYUVK/yK6qVkmqv6nizBaO\n/RAwRtKu+TE+LGkf4BHg9HwdSV0kdd9i31bP2VW6+r+GCv3yl7+kT58+9OvXj3333Zdzzz0XgH79\n+vGZz3yGfv36MXLkSH74wx82mitr65yVyllLI5WsqeSEtLK26ZuUJe0E/A7oQVZwdgeuJpujOjQi\n3pS0MiI6t3CM7wPHAa8AG4DbI+I+SQeRzXvtDuwA3BQRt0k6HLiVbL7r0fx5jpY0GjgkIi4uOPZF\nwNi8uQr4bEQskPRp4P+SDUe+A1wYEU9IuhY4G/gQ8Brws4i4uonMSbxDuWvXHixZsrDcMczMgJbf\npNzuP2FD0q4RsSZfHwd0i4h/b+MMkcp5rq2tTeKvr1RygrOWSipZU8kJlZe1peJVjhs22tqJkq4g\ne60Lye6cMDOzhFXklZek/sDPefe2dgFvR8Qnypdq26V05WVmVin+oYcNK4GLl5lZ8VoqXpXy8VBW\nIQrf51HJUskJzloqqWRNJSekldXFy8zMkuNhwzbgYUMzs+J52NDMzNoVFy9rJJUx71RygrOWSipZ\nU8kJaWV18TIzs+R4zqsNeM7LzKx4nvMyM7N2xcXLGkllzDuVnOCspZJK1lRyQlpZXbzMzCw5nvNq\nA57zMjMrnue8zMysXXHxskZSGfNOJSc4a6mkkjWVnJBWVhcvMzNLjue82oDnvMzMiuc5LzMza1dc\nvKyRVMa8U8kJzloqqWRNJSekldXFy8zMkuM5rzbgOS8zs+J5zsvMzNoVFy9rJJUx71RygrOWSipZ\nU8kJaWV18TIzs+R4zqsNeM7LzKx4nvMyM7N2xcXLGkllzDuVnOCspZJK1lRyQlpZXbyskbq6unJH\naJVUcoKzlkoqWVPJCWlldfGyRt56661yR2iVVHKCs5ZKKllTyQlpZXXxMjOz5Lh4WSMLFy4sd4RW\nSSUnOGuppJI1lZyQVlbfKt8GJPkkm5ltg+ZulXfxMjOz5HjY0MzMkuPiZWZmyXHxKjFJIyQ9J+kF\nSeMqIM9CSbMlPS3pibyvi6SHJT0v6SFJuxdsf4WkFyU9K2lYibPdJmmppDkFfUVnkzRI0pz8nN/U\nhlnHS1okaVa+jCh3VkkfkTRN0jOS5kq6OO+vuPPaRNaL8v6KOq+Sdpb0eP7/0FxJ4/P+SjynzWWt\nqHO6TSLCS4kWsj8O/hvoAewI1AF9y5zpJaDLFn3XAl/N18cB387X+wFPA1VAz/y1qITZjgKqgTnv\nJxvwOHBYvv4AMLyNso4HvtTEtgeUKyvQDajO1zsBzwN9K/G8tpC1Es9rx/zfHYAZwOGVeE5byFpx\n57TYxVdepXU48GJEvBwRG4BfASeXOZN47xX3ycAd+fodwCn5+ijgVxGxMSIWAi+SvaaSiIi/Asvf\nTzZJ3YDdImJmvt2dBfuUOitk53dLJ5cra0QsiYi6fH018CzwESrwvDaTdd/84Uo7r2vz1Z3JftEH\nFXhOW8gKFXZOi+XiVVr7Aq8WtBfx7v+M5RLAVEkzJZ2f93WNiKWQ/QIBPpj3b5l/MW2f/4NFZtuX\n7DzXa+tz/n8k1Un6WcGwUUVkldST7GpxBsX/zMuV9fG8q6LOq6QOkp4GlgBT81/qFXlOm8kKFXZO\ni+Xi9Y/nkxExCBgJXCjpaN79S6xeJb9/opKz/RDoFRHVZL8obihzngaSOgG/AS7Jr2oq9mfeRNaK\nO68RsTkiBpJdxR4u6UAq9Jw2kbUfFXhOi+XiVVqLge4F7Y/kfWUTEa/n//4d+B3ZMOBSSV0B8uGB\nZfnmi4H9CnYvR/5is5Utc0T8PfIJAeBW3h1iLWtWSVVkxeDnETEp767I89pU1ko9r3m2lUAtMIIK\nPadNZa3kc9paLl6lNRP4mKQeknYCzgQmlyuMpI75X7VI2hUYBszNM52bbzYaqP8FNxk4U9JOkvYH\nPgY8UeqYNB6LLypbPlyzQtLhkgScU7BPSbPmv7DqnQrMq5CstwPzI+Lmgr5KPa/vyVpp51XS3vXD\nbJJ2AY4nm5+ruHPaTNbnKu2cbpNy3i3yj7CQ/UX2PNnE5+VlzrI/2R2PT5MVrcvz/j2BP+U5Hwb2\nKNjnCrI7jp4FhpU43y+A14D1wCvAeUCXYrMBh+Sv70Xg5jbMeicwJz/HvyObAylrVuCTwKaCn/us\n/L/Jon/mZcxaUecVGJBnq8tzfW1b/z9qg3PaXNaKOqfbsvjjoczMLDkeNjQzs+S4eJmZWXJcvMzM\nLDkuXmZmlhwXLzMzS46Ll5mZJaeq3AHMbNtJ2gTMJnuzdACnRMQr5U1lVnp+n5dZwiStjIjObfh8\nO0TEprZ6PrPmeNjQLG1Nfa3Fuw9K3SQ9mn/h4BxJn8z7R0h6Kv+Swql5XxdJ9yv7stK/Seqf94+X\ndKekvwJ35p9Sfl3+JYd1ksaW/FWabcHDhmZp20XSLLIi9lJEnLbF42cDUyLiW/ln0nWUtDfwU+Co\niHhF0h75tt8AZkXEpyQNBX4ODMwfO4DsGwneyYvVWxExOP/MzsckPRwRL5f4tZo1cPEyS9vayL7i\npjkzgdsk7QhMiojZeWF6tH5uLCLeyrc9iuxDWomI6ZL2rP8gZ2ByRLyTrw8DBkj6dN7uDPQGXLys\nzbh4mbVjEfEXSccAJwITJN0IvEXTw40tTYCvKVgXcFFETN1+Sc2K4zkvs7Rtbc6rO7AsIm4DbgMG\nkX2T8tGSeuTbdMk3/wvw2byvBngjsi+D3NJDwAX5d28hqXf+dRtmbcZXXmZp29rtwjXAZZI2AKuA\ncyLiDUlfAO7P58GWAcPJ5rxulzSb7ErrnGaO+TOgJzCrYP9T3u8LMSuGb5U3M7PkeNjQzMyS4+Jl\nZmbJcfEyM7PkuHiZmVlyXLzMzCw5Ll5mZpYcFy8zM0uOi5eZmSXn/wPZg3v2gpXDZQAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118194c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot feature importance using built-in function\n",
    "from xgboost import XGBClassifier\n",
    "from xgboost import plot_importance\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#使用pandas重新将数据载入到内存空间中\n",
    "dataset1=pd.read_csv('WeiboProfile_train_final.csv')\n",
    "\n",
    "train_names=['out_degree1','in_degree1','all_degree1','bi_degree1','pagerank1']\n",
    "\n",
    "X = pd.DataFrame(dataset,columns=train_names)\n",
    "y = pd.DataFrame(dataset,columns=[\"scaleT75\"])\n",
    "\n",
    "# split data into train and test sets\n",
    "#seed = 7\n",
    "#test_size = 0.2\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=seed)\n",
    "\n",
    "# fit model on training data\n",
    "model = XGBClassifier()\n",
    "model.fit(X, y)\n",
    "\n",
    "# plot feature importance\n",
    "plot_importance(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}