Lecture-18-AutoSummarization-Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auto Summarization\n",
    "Main Reference: \n",
    "1. TextRank: https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf\n",
    "2. SentenceEmbedding: \n",
    "3. InformationRetrievalAndTextMining: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/ibm/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/anaconda3/envs/ibm/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import jieba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cut(string): return ' '.join(jieba.cut(string))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "news_file = 'sqlResult_1558435.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "news_content = pd.read_csv(news_file, encoding='gb18030')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>author</th>\n",
       "      <th>source</th>\n",
       "      <th>content</th>\n",
       "      <th>feature</th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>89617</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快科技@http://www.kkj.cn/</td>\n",
       "      <td>此外，自本周（6月12日）起，除小米手机6等15款机型外，其余机型已暂停更新发布（含开发版/...</td>\n",
       "      <td>{\"type\":\"科技\",\"site\":\"cnbeta\",\"commentNum\":\"37\"...</td>\n",
       "      <td>小米MIUI 9首批机型曝光：共计15款</td>\n",
       "      <td>http://www.cnbeta.com/articles/tech/623597.htm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89616</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快科技@http://www.kkj.cn/</td>\n",
       "      <td>骁龙835作为唯一通过Windows 10桌面平台认证的ARM处理器，高通强调，不会因为只考...</td>\n",
       "      <td>{\"type\":\"科技\",\"site\":\"cnbeta\",\"commentNum\":\"15\"...</td>\n",
       "      <td>骁龙835在Windows 10上的性能表现有望改善</td>\n",
       "      <td>http://www.cnbeta.com/articles/tech/623599.htm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快科技@http://www.kkj.cn/</td>\n",
       "      <td>此前的一加3T搭载的是3400mAh电池，DashCharge快充规格为5V/4A。\\r\\n...</td>\n",
       "      <td>{\"type\":\"科技\",\"site\":\"cnbeta\",\"commentNum\":\"18\"...</td>\n",
       "      <td>一加手机5细节曝光：3300mAh、充半小时用1天</td>\n",
       "      <td>http://www.cnbeta.com/articles/tech/623601.htm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>新华社</td>\n",
       "      <td>这是6月18日在葡萄牙中部大佩德罗冈地区拍摄的被森林大火烧毁的汽车。新华社记者张立云摄\\r\\n</td>\n",
       "      <td>{\"type\":\"国际新闻\",\"site\":\"环球\",\"commentNum\":\"0\",\"j...</td>\n",
       "      <td>葡森林火灾造成至少62人死亡 政府宣布进入紧急状态（组图）</td>\n",
       "      <td>http://world.huanqiu.com/hot/2017-06/10866126....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>89613</td>\n",
       "      <td>胡淑丽_MN7479</td>\n",
       "      <td>深圳大件事</td>\n",
       "      <td>（原标题：44岁女子跑深圳约会网友被拒，暴雨中裸身奔走……）\\r\\n@深圳交警微博称：昨日清...</td>\n",
       "      <td>{\"type\":\"新闻\",\"site\":\"网易热门\",\"commentNum\":\"978\",...</td>\n",
       "      <td>44岁女子约网友被拒暴雨中裸奔 交警为其披衣相随</td>\n",
       "      <td>http://news.163.com/17/0618/00/CN617P3Q0001875...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id      author                  source  \\\n",
       "0  89617         NaN  快科技@http://www.kkj.cn/   \n",
       "1  89616         NaN  快科技@http://www.kkj.cn/   \n",
       "2  89615         NaN  快科技@http://www.kkj.cn/   \n",
       "3  89614         NaN                     新华社   \n",
       "4  89613  胡淑丽_MN7479                   深圳大件事   \n",
       "\n",
       "                                             content  \\\n",
       "0  此外，自本周（6月12日）起，除小米手机6等15款机型外，其余机型已暂停更新发布（含开发版/...   \n",
       "1  骁龙835作为唯一通过Windows 10桌面平台认证的ARM处理器，高通强调，不会因为只考...   \n",
       "2  此前的一加3T搭载的是3400mAh电池，DashCharge快充规格为5V/4A。\\r\\n...   \n",
       "3    这是6月18日在葡萄牙中部大佩德罗冈地区拍摄的被森林大火烧毁的汽车。新华社记者张立云摄\\r\\n   \n",
       "4  （原标题：44岁女子跑深圳约会网友被拒，暴雨中裸身奔走……）\\r\\n@深圳交警微博称：昨日清...   \n",
       "\n",
       "                                             feature  \\\n",
       "0  {\"type\":\"科技\",\"site\":\"cnbeta\",\"commentNum\":\"37\"...   \n",
       "1  {\"type\":\"科技\",\"site\":\"cnbeta\",\"commentNum\":\"15\"...   \n",
       "2  {\"type\":\"科技\",\"site\":\"cnbeta\",\"commentNum\":\"18\"...   \n",
       "3  {\"type\":\"国际新闻\",\"site\":\"环球\",\"commentNum\":\"0\",\"j...   \n",
       "4  {\"type\":\"新闻\",\"site\":\"网易热门\",\"commentNum\":\"978\",...   \n",
       "\n",
       "                           title  \\\n",
       "0           小米MIUI 9首批机型曝光：共计15款   \n",
       "1     骁龙835在Windows 10上的性能表现有望改善   \n",
       "2      一加手机5细节曝光：3300mAh、充半小时用1天   \n",
       "3  葡森林火灾造成至少62人死亡 政府宣布进入紧急状态（组图）   \n",
       "4       44岁女子约网友被拒暴雨中裸奔 交警为其披衣相随   \n",
       "\n",
       "                                                 url  \n",
       "0     http://www.cnbeta.com/articles/tech/623597.htm  \n",
       "1     http://www.cnbeta.com/articles/tech/623599.htm  \n",
       "2     http://www.cnbeta.com/articles/tech/623601.htm  \n",
       "3  http://world.huanqiu.com/hot/2017-06/10866126....  \n",
       "4  http://news.163.com/17/0618/00/CN617P3Q0001875...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news_content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pure_content = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pure_content['content'] = news_content['content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pure_content = pure_content.fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pure_content['tokenized_content'] = pure_content['content'].apply(cut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content</th>\n",
       "      <th>tokenized_content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>此外，自本周（6月12日）起，除小米手机6等15款机型外，其余机型已暂停更新发布（含开发版/...</td>\n",
       "      <td>此外 ， 自 本周 （ 6 月 12 日 ） 起 ， 除 小米 手机 6 等 15 款 机型...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>骁龙835作为唯一通过Windows 10桌面平台认证的ARM处理器，高通强调，不会因为只考...</td>\n",
       "      <td>骁龙 835 作为 唯一 通过 Windows   10 桌面 平台 认证 的 ARM 处理...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>此前的一加3T搭载的是3400mAh电池，DashCharge快充规格为5V/4A。\\r\\n...</td>\n",
       "      <td>此前 的 一加 3T 搭载 的 是 3400mAh 电池 ， DashCharge 快充 规...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>这是6月18日在葡萄牙中部大佩德罗冈地区拍摄的被森林大火烧毁的汽车。新华社记者张立云摄\\r\\n</td>\n",
       "      <td>这是 6 月 18 日 在 葡萄牙 中部 大 佩德罗 冈 地区 拍摄 的 被 森林 大火 烧...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>（原标题：44岁女子跑深圳约会网友被拒，暴雨中裸身奔走……）\\r\\n@深圳交警微博称：昨日清...</td>\n",
       "      <td>（ 原 标题 ： 44 岁 女子 跑 深圳 约会 网友 被 拒 ， 暴雨 中 裸身 奔走 …...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             content  \\\n",
       "0  此外，自本周（6月12日）起，除小米手机6等15款机型外，其余机型已暂停更新发布（含开发版/...   \n",
       "1  骁龙835作为唯一通过Windows 10桌面平台认证的ARM处理器，高通强调，不会因为只考...   \n",
       "2  此前的一加3T搭载的是3400mAh电池，DashCharge快充规格为5V/4A。\\r\\n...   \n",
       "3    这是6月18日在葡萄牙中部大佩德罗冈地区拍摄的被森林大火烧毁的汽车。新华社记者张立云摄\\r\\n   \n",
       "4  （原标题：44岁女子跑深圳约会网友被拒，暴雨中裸身奔走……）\\r\\n@深圳交警微博称：昨日清...   \n",
       "\n",
       "                                   tokenized_content  \n",
       "0  此外 ， 自 本周 （ 6 月 12 日 ） 起 ， 除 小米 手机 6 等 15 款 机型...  \n",
       "1  骁龙 835 作为 唯一 通过 Windows   10 桌面 平台 认证 的 ARM 处理...  \n",
       "2  此前 的 一加 3T 搭载 的 是 3400mAh 电池 ， DashCharge 快充 规...  \n",
       "3  这是 6 月 18 日 在 葡萄牙 中部 大 佩德罗 冈 地区 拍摄 的 被 森林 大火 烧...  \n",
       "4  （ 原 标题 ： 44 岁 女子 跑 深圳 约会 网友 被 拒 ， 暴雨 中 裸身 奔走 …...  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pure_content.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the word vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import FastText"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('all_corpus.txt', 'w', encoding='utf-8') as f:\n",
    "    f.write(' '.join(pure_content['tokenized_content'].tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models.word2vec import LineSentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = FastText(LineSentence('all_corpus.txt'), window=5, size=35, iter=10, min_count=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import reduce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pure_content['tok,,'].tolist() \n",
    "[[''], [''], [''], [''], [''], [''], [''], ['']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduce(lambda a, b: a + b, pure_content['tokenized_content'].tolist()).split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokeners = [t for l in pure_content['tokenized_content'].tolist() for t in l.split()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['此外', '，', '自', '本周', '（', '6', '月', '12', '日', '）']"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokeners[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counts the words occurence "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokener_counter = Counter(tokeners)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "frequence = {w: count/len(tokeners) for w, count in tokener_counter.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "occurences_frequences = sorted(list(frequence.values()), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = range(len(occurences_frequences))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1322193c8>]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/ibm/lib/python3.6/site-packages/matplotlib/font_manager.py:1328: UserWarning: findfont: Font family ['/Users/mqgao/Downloads/SourceHanSerifSC-Regular.otf'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X[:50], occurences_frequences[:50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'此': 33626,\n",
       "         '外': 131119,\n",
       "         ' ': 22994303,\n",
       "         '，': 1360799,\n",
       "         '自': 63122,\n",
       "         '本': 53481,\n",
       "         '周': 25145,\n",
       "         '（': 212529,\n",
       "         '6': 97092,\n",
       "         '月': 205888,\n",
       "         '1': 318823,\n",
       "         '2': 275952,\n",
       "         '日': 278379,\n",
       "         '）': 212782,\n",
       "         '起': 33322,\n",
       "         '除': 9959,\n",
       "         '小': 51778,\n",
       "         '米': 21697,\n",
       "         '手': 52263,\n",
       "         '机': 65294,\n",
       "         '等': 65840,\n",
       "         '5': 139557,\n",
       "         '款': 10820,\n",
       "         '型': 18257,\n",
       "         '其': 43675,\n",
       "         '余': 11292,\n",
       "         '已': 39217,\n",
       "         '暂': 2975,\n",
       "         '停': 7879,\n",
       "         '更': 32969,\n",
       "         '新': 272577,\n",
       "         '发': 144500,\n",
       "         '布': 38003,\n",
       "         '含': 3394,\n",
       "         '开': 88713,\n",
       "         '版': 6059,\n",
       "         '/': 33916,\n",
       "         '体': 89027,\n",
       "         '验': 16007,\n",
       "         '内': 55577,\n",
       "         '测': 10007,\n",
       "         '稳': 9068,\n",
       "         '定': 46305,\n",
       "         '不': 125980,\n",
       "         '受': 30189,\n",
       "         '影': 32483,\n",
       "         '响': 12528,\n",
       "         '以': 102150,\n",
       "         '确': 15469,\n",
       "         '保': 44264,\n",
       "         '工': 77783,\n",
       "         '程': 28967,\n",
       "         '师': 18739,\n",
       "         '可': 49642,\n",
       "         '集': 24404,\n",
       "         '中': 280076,\n",
       "         '全': 88651,\n",
       "         '部': 78994,\n",
       "         '精': 14930,\n",
       "         '力': 60323,\n",
       "         '进': 88523,\n",
       "         '行': 137683,\n",
       "         '系': 36449,\n",
       "         '统': 38829,\n",
       "         '优': 16057,\n",
       "         '化': 64405,\n",
       "         '作': 102091,\n",
       "         '。': 636522,\n",
       "         '有': 144978,\n",
       "         '人': 198466,\n",
       "         '猜': 439,\n",
       "         '这': 92182,\n",
       "         '也': 49389,\n",
       "         '是': 158493,\n",
       "         '将': 71078,\n",
       "         '主': 67385,\n",
       "         '要': 80169,\n",
       "         '用': 63684,\n",
       "         '到': 82450,\n",
       "         'M': 5581,\n",
       "         'I': 9653,\n",
       "         'U': 2126,\n",
       "         '9': 54909,\n",
       "         '的': 713561,\n",
       "         '研': 28687,\n",
       "         '之': 48620,\n",
       "         '\\r': 94098,\n",
       "         '\\n': 94388,\n",
       "         '8': 55320,\n",
       "         '去': 25533,\n",
       "         '年': 221905,\n",
       "         '距': 3737,\n",
       "         '今': 29716,\n",
       "         '一': 287295,\n",
       "         '时': 103642,\n",
       "         '候': 9617,\n",
       "         '换': 5861,\n",
       "         '代': 112166,\n",
       "         '了': 139942,\n",
       "         '当': 71881,\n",
       "         '然': 23643,\n",
       "         '关': 58426,\n",
       "         '于': 72657,\n",
       "         '切': 11383,\n",
       "         '信': 32998,\n",
       "         '息': 20369,\n",
       "         '我': 55082,\n",
       "         '们': 54185,\n",
       "         '还': 35973,\n",
       "         '待': 7233,\n",
       "         '官': 10953,\n",
       "         '方': 79436,\n",
       "         '消': 16841,\n",
       "         '骁': 249,\n",
       "         '龙': 19712,\n",
       "         '3': 102841,\n",
       "         '为': 141563,\n",
       "         '唯': 1900,\n",
       "         '通': 55915,\n",
       "         '过': 61809,\n",
       "         'W': 1567,\n",
       "         'i': 8929,\n",
       "         'n': 678457,\n",
       "         'd': 3382,\n",
       "         'o': 9806,\n",
       "         'w': 1825,\n",
       "         's': 5809,\n",
       "         '0': 268186,\n",
       "         '桌': 1098,\n",
       "         '面': 51623,\n",
       "         '平': 53294,\n",
       "         '台': 38334,\n",
       "         '认': 22808,\n",
       "         '证': 19798,\n",
       "         'A': 17402,\n",
       "         'R': 2629,\n",
       "         '处': 25483,\n",
       "         '理': 55145,\n",
       "         '器': 13649,\n",
       "         '高': 71760,\n",
       "         '强': 39032,\n",
       "         '调': 28635,\n",
       "         '会': 123619,\n",
       "         '因': 26708,\n",
       "         '只': 18060,\n",
       "         '考': 19017,\n",
       "         '虑': 3188,\n",
       "         '性': 33447,\n",
       "         '能': 73153,\n",
       "         '而': 34498,\n",
       "         '屏': 1408,\n",
       "         '蔽': 220,\n",
       "         '掉': 1888,\n",
       "         '核': 11624,\n",
       "         '心': 43569,\n",
       "         '相': 37663,\n",
       "         '反': 15270,\n",
       "         '他': 60014,\n",
       "         '正': 34420,\n",
       "         '联': 65344,\n",
       "         '微': 8886,\n",
       "         '软': 3347,\n",
       "         '找': 5501,\n",
       "         '种': 28906,\n",
       "         '适': 5706,\n",
       "         '合': 80592,\n",
       "         '、': 296644,\n",
       "         '兼': 2257,\n",
       "         '顾': 4303,\n",
       "         '和': 147115,\n",
       "         '功': 12763,\n",
       "         '耗': 1281,\n",
       "         '完': 36684,\n",
       "         '美': 71913,\n",
       "         '案': 20720,\n",
       "         '报': 41379,\n",
       "         '道': 39527,\n",
       "         '称': 16972,\n",
       "         '经': 75984,\n",
       "         '拿': 7588,\n",
       "         '些': 24295,\n",
       "         '源': 20833,\n",
       "         '码': 4305,\n",
       "         '便': 9169,\n",
       "         '好': 37765,\n",
       "         '地': 107688,\n",
       "         '解': 26095,\n",
       "         'b': 1699,\n",
       "         'g': 2715,\n",
       "         '.': 39820,\n",
       "         'l': 4806,\n",
       "         't': 7208,\n",
       "         'e': 11837,\n",
       "         '架': 5994,\n",
       "         '构': 19167,\n",
       "         '资': 55685,\n",
       "         '料': 9616,\n",
       "         '显': 18265,\n",
       "         '示': 44524,\n",
       "         '成': 100335,\n",
       "         'C': 11190,\n",
       "         'P': 9435,\n",
       "         'G': 3435,\n",
       "         '基': 42267,\n",
       "         '带': 39666,\n",
       "         '蓝': 3472,\n",
       "         '牙': 8642,\n",
       "         '-': 18797,\n",
       "         'F': 3338,\n",
       "         'S': 6143,\n",
       "         '比': 90066,\n",
       "         '传': 25120,\n",
       "         '节': 30470,\n",
       "         '省': 36146,\n",
       "         '至': 31956,\n",
       "         '少': 20769,\n",
       "         '%': 21051,\n",
       "         'B': 10060,\n",
       "         '空': 23692,\n",
       "         '间': 40981,\n",
       "         '按': 8322,\n",
       "         '计': 37729,\n",
       "         '划': 16983,\n",
       "         'Q': 701,\n",
       "         '4': 125823,\n",
       "         '华': 175322,\n",
       "         '硕': 976,\n",
       "         '惠': 5548,\n",
       "         '普': 22879,\n",
       "         '想': 15162,\n",
       "         '首': 39531,\n",
       "         '电': 64150,\n",
       "         '脑': 3520,\n",
       "         '预': 18297,\n",
       "         '均': 10540,\n",
       "         '二': 88158,\n",
       "         '形': 15730,\n",
       "         '态': 16535,\n",
       "         '产': 65820,\n",
       "         '品': 47132,\n",
       "         '个': 93876,\n",
       "         '始': 15083,\n",
       "         '未': 20018,\n",
       "         '来': 95755,\n",
       "         '许': 10467,\n",
       "         '见': 14711,\n",
       "         '三': 39604,\n",
       "         '星': 13585,\n",
       "         'E': 3324,\n",
       "         'x': 1267,\n",
       "         'y': 1818,\n",
       "         '科': 47828,\n",
       "         '麒': 153,\n",
       "         '麟': 448,\n",
       "         '澎': 317,\n",
       "         '湃': 278,\n",
       "         '入': 46225,\n",
       "         '前': 72681,\n",
       "         '加': 67983,\n",
       "         'T': 4617,\n",
       "         '搭': 3068,\n",
       "         '载': 7023,\n",
       "         'm': 3972,\n",
       "         'h': 4615,\n",
       "         '池': 2268,\n",
       "         'D': 3631,\n",
       "         'a': 11039,\n",
       "         'r': 7160,\n",
       "         '快': 17466,\n",
       "         '充': 7062,\n",
       "         '规': 30956,\n",
       "         '格': 29668,\n",
       "         'V': 1701,\n",
       "         '缩': 2911,\n",
       "         '水': 39902,\n",
       "         '与': 70298,\n",
       "         '刘': 10969,\n",
       "         '虎': 2394,\n",
       "         '所': 32206,\n",
       "         '说': 56933,\n",
       "         '做': 17816,\n",
       "         '市': 96112,\n",
       "         '最': 45276,\n",
       "         '轻': 7406,\n",
       "         '薄': 1054,\n",
       "         '大': 182608,\n",
       "         '旗': 3267,\n",
       "         '舰': 2979,\n",
       "         '设': 48738,\n",
       "         '照': 62543,\n",
       "         '目': 51833,\n",
       "         '掌': 2646,\n",
       "         '握': 3006,\n",
       "         '拥': 4364,\n",
       "         '寸': 514,\n",
       "         'O': 5336,\n",
       "         'L': 2120,\n",
       "         '双': 17298,\n",
       "         '万': 37176,\n",
       "         '摄': 47867,\n",
       "         '像': 7745,\n",
       "         '头': 20210,\n",
       "         '备': 18182,\n",
       "         '货': 11282,\n",
       "         '量': 33847,\n",
       "         '“': 239982,\n",
       "         '惊': 2364,\n",
       "         '喜': 5031,\n",
       "         '”': 239981,\n",
       "         '根': 13853,\n",
       "         '据': 38890,\n",
       "         '京': 43424,\n",
       "         '东': 40533,\n",
       "         '泄': 1001,\n",
       "         '露': 5243,\n",
       "         '售': 9799,\n",
       "         '价': 26942,\n",
       "         '元': 44634,\n",
       "         '应': 29665,\n",
       "         '该': 18218,\n",
       "         '在': 306813,\n",
       "         '7': 135735,\n",
       "         '某': 8047,\n",
       "         '葡': 2956,\n",
       "         '萄': 2444,\n",
       "         '佩': 3218,\n",
       "         '德': 46290,\n",
       "         '罗': 34120,\n",
       "         '冈': 1040,\n",
       "         '区': 72543,\n",
       "         '拍': 10087,\n",
       "         '被': 29863,\n",
       "         '森': 9157,\n",
       "         '林': 22160,\n",
       "         '火': 12989,\n",
       "         '烧': 1719,\n",
       "         '毁': 1326,\n",
       "         '汽': 8246,\n",
       "         '车': 45020,\n",
       "         '社': 179122,\n",
       "         '记': 78283,\n",
       "         '者': 102702,\n",
       "         '张': 22768,\n",
       "         '立': 30649,\n",
       "         '云': 9415,\n",
       "         '原': 22157,\n",
       "         '标': 31950,\n",
       "         '题': 37097,\n",
       "         '：': 102478,\n",
       "         '岁': 10161,\n",
       "         '女': 24376,\n",
       "         '子': 50908,\n",
       "         '跑': 6544,\n",
       "         '深': 23523,\n",
       "         '圳': 3463,\n",
       "         '约': 27179,\n",
       "         '网': 49881,\n",
       "         '友': 13160,\n",
       "         '拒': 1556,\n",
       "         '暴': 3898,\n",
       "         '雨': 7829,\n",
       "         '裸': 320,\n",
       "         '身': 20746,\n",
       "         '奔': 1744,\n",
       "         '走': 18045,\n",
       "         '…': 12053,\n",
       "         '@': 405,\n",
       "         '交': 44445,\n",
       "         '警': 16206,\n",
       "         '博': 16408,\n",
       "         '昨': 1373,\n",
       "         '清': 16048,\n",
       "         '晨': 3678,\n",
       "         '现': 67811,\n",
       "         '赤': 880,\n",
       "         '上': 132654,\n",
       "         '南': 41830,\n",
       "         '坪': 912,\n",
       "         '速': 15401,\n",
       "         '期': 43884,\n",
       "         '生': 95604,\n",
       "         '辅': 2013,\n",
       "         '后': 68324,\n",
       "         '赶': 2278,\n",
       "         '紧': 8117,\n",
       "         '披': 1487,\n",
       "         '黄': 13860,\n",
       "         '衣': 2503,\n",
       "         '并': 34519,\n",
       "         '路': 60794,\n",
       "         '劝': 652,\n",
       "         '她': 13918,\n",
       "         '那': 13133,\n",
       "         '么': 11218,\n",
       "         '事': 50913,\n",
       "         '底': 10393,\n",
       "         '都': 41015,\n",
       "         '什': 9308,\n",
       "         '呢': 1525,\n",
       "         '？': 13642,\n",
       "         '您': 737,\n",
       "         '场': 79516,\n",
       "         '岗': 3023,\n",
       "         '队': 82589,\n",
       "         '坂': 46,\n",
       "         '田': 7622,\n",
       "         '青': 17573,\n",
       "         '位': 36148,\n",
       "         '表': 55027,\n",
       "         '帅': 2564,\n",
       "         '气': 18438,\n",
       "         '话': 14014,\n",
       "         '略': 8925,\n",
       "         '腼': 55,\n",
       "         '腆': 55,\n",
       "         '介': 12541,\n",
       "         '绍': 9726,\n",
       "         '早': 8263,\n",
       "         '分': 74980,\n",
       "         '环': 24412,\n",
       "         '城': 37600,\n",
       "         '附': 5400,\n",
       "         '近': 36735,\n",
       "         '值': 12681,\n",
       "         '勤': 1410,\n",
       "         '接': 28908,\n",
       "         '动': 89921,\n",
       "         '危': 4637,\n",
       "         '险': 12987,\n",
       "         '情': 26915,\n",
       "         '随': 12714,\n",
       "         '骑': 4173,\n",
       "         '着': 32919,\n",
       "         '铁': 16116,\n",
       "         '沿': 6238,\n",
       "         '寻': 3972,\n",
       "         '概': 2056,\n",
       "         '花': 19968,\n",
       "         '十': 20506,\n",
       "         '多': 84668,\n",
       "         '钟': 6146,\n",
       "         '出': 95566,\n",
       "         '口': 22757,\n",
       "         '往': 11338,\n",
       "         '向': 31220,\n",
       "         '逆': 1746,\n",
       "         '丝': 7765,\n",
       "         '挂': 3222,\n",
       "         '流': 24685,\n",
       "         '坐': 3332,\n",
       "         '躺': 327,\n",
       "         '象': 10962,\n",
       "         '另': 6744,\n",
       "         '名': 50547,\n",
       "         '巡': 5117,\n",
       "         '防': 15805,\n",
       "         '员': 69303,\n",
       "         '追': 5563,\n",
       "         '绪': 1287,\n",
       "         '很': 20253,\n",
       "         '低': 13091,\n",
       "         '落': 15637,\n",
       "         '尝': 1509,\n",
       "         '试': 15115,\n",
       "         '离': 10021,\n",
       "         '愿': 8929,\n",
       "         '意': 35619,\n",
       "         '继': 10650,\n",
       "         '续': 23373,\n",
       "         '缓': 3289,\n",
       "         '慢': 2445,\n",
       "         '干': 13444,\n",
       "         '边': 13644,\n",
       "         '聚': 5587,\n",
       "         '民': 72357,\n",
       "         '围': 10977,\n",
       "         '观': 20073,\n",
       "         '刺': 4208,\n",
       "         '激': 5772,\n",
       "         '盯': 361,\n",
       "         '驱': 2175,\n",
       "         '群': 14310,\n",
       "         '众': 21034,\n",
       "         '从': 41533,\n",
       "         '提': 45973,\n",
       "         '供': 21958,\n",
       "         '份': 13916,\n",
       "         '视': 19607,\n",
       "         '频': 5997,\n",
       "         '雪': 6236,\n",
       "         '监': 16679,\n",
       "         '控': 12000,\n",
       "         '穿': 3924,\n",
       "         '白': 14392,\n",
       "         '色': 18257,\n",
       "         '裤': 431,\n",
       "         '班': 14009,\n",
       "         '峰': 9702,\n",
       "         '字': 12050,\n",
       "         '排': 12593,\n",
       "         '长': 64494,\n",
       "         '纷': 4310,\n",
       "         '驻': 6178,\n",
       "         '足': 36127,\n",
       "         '望': 14077,\n",
       "         '辆': 5474,\n",
       "         '放': 23405,\n",
       "         '度': 47087,\n",
       "         '但': 28400,\n",
       "         '下': 62764,\n",
       "         '脚': 3075,\n",
       "         '步': 22722,\n",
       "         '依': 10737,\n",
       "         '挡': 674,\n",
       "         '住': 12468,\n",
       "         '镜': 2113,\n",
       "         '再': 14231,\n",
       "         '次': 46994,\n",
       "         '没': 20273,\n",
       "         '朝': 7907,\n",
       "         '尾': 1655,\n",
       "         '或': 14946,\n",
       "         '止': 6418,\n",
       "         '看': 25393,\n",
       "         '样': 19590,\n",
       "         '况': 13199,\n",
       "         '恐': 6070,\n",
       "         '怕': 1533,\n",
       "         '家': 100513,\n",
       "         '办': 27192,\n",
       "         '法': 81209,\n",
       "         '淡': 1127,\n",
       "         '对': 97773,\n",
       "         '敢': 1558,\n",
       "         '里': 55821,\n",
       "         '挺': 993,\n",
       "         '别': 18903,\n",
       "         '扭': 581,\n",
       "         '感': 14856,\n",
       "         '觉': 5668,\n",
       "         '尴': 286,\n",
       "         '尬': 293,\n",
       "         '跟': 4464,\n",
       "         '让': 21393,\n",
       "         '举': 44650,\n",
       "         '突': 10460,\n",
       "         '靠': 4377,\n",
       "         '护': 18128,\n",
       "         '栏': 1559,\n",
       "         '跳': 2922,\n",
       "         '忙': 2601,\n",
       "         '冲': 6377,\n",
       "         '拉': 41852,\n",
       "         '制': 47572,\n",
       "         '远': 10716,\n",
       "         '碍': 1936,\n",
       "         '服': 29957,\n",
       "         '递': 2865,\n",
       "         '把': 13983,\n",
       "         '扔': 316,\n",
       "         '沟': 4070,\n",
       "         '耐': 1102,\n",
       "         '导': 26560,\n",
       "         '安': 51131,\n",
       "         '慰': 1250,\n",
       "         '听': 5063,\n",
       "         '断': 12399,\n",
       "         '重': 57943,\n",
       "         '复': 13392,\n",
       "         '句': 1395,\n",
       "         '你': 8315,\n",
       "         '遭': 5049,\n",
       "         '遇': 8377,\n",
       "         '图': 24493,\n",
       "         '挣': 461,\n",
       "         '脱': 7865,\n",
       "         '就': 44830,\n",
       "         '牵': 1346,\n",
       "         '天': 52990,\n",
       "         '连': 14034,\n",
       "         '眼': 6846,\n",
       "         '睛': 592,\n",
       "         '睁': 133,\n",
       "         '瞬': 667,\n",
       "         '透': 11006,\n",
       "         '帮': 7627,\n",
       "         '助': 18523,\n",
       "         '冒': 1083,\n",
       "         '四': 23415,\n",
       "         '吧': 1567,\n",
       "         '回': 26887,\n",
       "         '片': 68028,\n",
       "         '翼': 1692,\n",
       "         '旁': 1548,\n",
       "         '景': 14103,\n",
       "         '派': 6158,\n",
       "         '姑': 789,\n",
       "         '娘': 1398,\n",
       "         '才': 13934,\n",
       "         '姓': 2413,\n",
       "         '陈': 11621,\n",
       "         '湖': 13151,\n",
       "         '北': 59972,\n",
       "         '属': 7552,\n",
       "         '映': 6195,\n",
       "         '神': 10667,\n",
       "         '病': 10887,\n",
       "         '史': 11627,\n",
       "         '老': 21406,\n",
       "         '挫': 388,\n",
       "         '致': 13527,\n",
       "         '异': 3810,\n",
       "         '常': 23842,\n",
       "         '遂': 348,\n",
       "         '念': 9022,\n",
       "         '送': 7364,\n",
       "         '医': 22824,\n",
       "         '院': 35408,\n",
       "         '治': 24805,\n",
       "         '疗': 8880,\n",
       "         '君': 1446,\n",
       "         '希': 14126,\n",
       "         '点': 41022,\n",
       "         '康': 9734,\n",
       "         '实': 62440,\n",
       "         '真': 10783,\n",
       "         '爱': 12029,\n",
       "         '存': 12146,\n",
       "         '龄': 2464,\n",
       "         '限': 14147,\n",
       "         '善': 9933,\n",
       "         '良': 6544,\n",
       "         '暖': 2504,\n",
       "         '男': 15160,\n",
       "         'Z': 437,\n",
       "         'N': 7543,\n",
       "         '弓': 234,\n",
       "         '桦': 92,\n",
       "         '问': 28093,\n",
       "         '哥': 10507,\n",
       "         '票': 10301,\n",
       "         '吗': 1854,\n",
       "         '谅': 292,\n",
       "         '辈': 1127,\n",
       "         '浪': 3207,\n",
       "         '荡': 1537,\n",
       "         '羁': 78,\n",
       "         '萨': 9870,\n",
       "         '摩': 7774,\n",
       "         '耶': 2812,\n",
       "         '嘛': 123,\n",
       "         '己': 14467,\n",
       "         '同': 57783,\n",
       "         '谢': 4604,\n",
       "         '蜀': 165,\n",
       "         '黍': 25,\n",
       "         '聆': 211,\n",
       "         '灵': 2678,\n",
       "         '赞': 3082,\n",
       "         '華': 2,\n",
       "         '盾': 1532,\n",
       "         '察': 11245,\n",
       "         '需': 19398,\n",
       "         '申': 7065,\n",
       "         '援': 5191,\n",
       "         'c': 4252,\n",
       "         '！': 6638,\n",
       "         'J': 719,\n",
       "         '李': 18497,\n",
       "         '赫': 4405,\n",
       "         '海': 60612,\n",
       "         '木': 6777,\n",
       "         '迷': 3786,\n",
       "         '奸': 167,\n",
       "         '号': 17374,\n",
       "         '午': 8758,\n",
       "         '淮': 600,\n",
       "         '涟': 46,\n",
       "         '县': 18750,\n",
       "         '公': 91295,\n",
       "         '局': 31205,\n",
       "         '刑': 3098,\n",
       "         '奇': 11048,\n",
       "         '怪': 588,\n",
       "         '言': 10646,\n",
       "         '语': 7143,\n",
       "         '声': 10305,\n",
       "         '侵': 2671,\n",
       "         '害': 6076,\n",
       "         '几': 11853,\n",
       "         '键': 5129,\n",
       "         '词': 2897,\n",
       "         '令': 6312,\n",
       "         '\\u3000': 408918,\n",
       "         '股': 20842,\n",
       "         '纳': 26371,\n",
       "         '指': 25538,\n",
       "         '数': 39689,\n",
       "         '利': 67605,\n",
       "         '演': 20301,\n",
       "         '马': 46514,\n",
       "         '金': 52262,\n",
       "         '融': 19419,\n",
       "         '盘': 7021,\n",
       "         '仅': 12138,\n",
       "         '板': 8192,\n",
       "         '块': 4511,\n",
       "         '仍': 7844,\n",
       "         '维': 21709,\n",
       "         '红': 11648,\n",
       "         '状': 6105,\n",
       "         '析': 5592,\n",
       "         '士': 24428,\n",
       "         '益': 13385,\n",
       "         '估': 3453,\n",
       "         '获': 22628,\n",
       "         '睐': 433,\n",
       "         '弈': 353,\n",
       "         '风': 22232,\n",
       "         '替': 1730,\n",
       "         '震': 3630,\n",
       "         '延': 5744,\n",
       "         '改': 28927,\n",
       "         '济': 30650,\n",
       "         '悲': 661,\n",
       "         '修': 8831,\n",
       "         '支': 24542,\n",
       "         '撑': 2356,\n",
       "         '逐': 7042,\n",
       "         '九': 4824,\n",
       "         '势': 17100,\n",
       "         '两': 39190,\n",
       "         '幅': 9399,\n",
       "         '级': 43380,\n",
       "         '业': 125430,\n",
       "         '收': 29716,\n",
       "         '银': 15166,\n",
       "         '非': 19685,\n",
       "         '涨': 10346,\n",
       "         '商': 29875,\n",
       "         '达': 32570,\n",
       "         '国': 340445,\n",
       "         '托': 9436,\n",
       "         '及': 38224,\n",
       "         '券': 6173,\n",
       "         '共': 37321,\n",
       "         '招': 5944,\n",
       "         '贵': 7066,\n",
       "         '阳': 13240,\n",
       "         '夏': 7182,\n",
       "         '浦': 2484,\n",
       "         '兴': 10442,\n",
       "         '超': 26806,\n",
       "         '太': 11334,\n",
       "         '居': 10928,\n",
       "         '西': 58840,\n",
       "         '寿': 1046,\n",
       "         '跌': 7360,\n",
       "         '宣': 11246,\n",
       "         '占': 8236,\n",
       "         '团': 24553,\n",
       "         '包': 17117,\n",
       "         '剔': 188,\n",
       "         '互': 16853,\n",
       "         '易': 21229,\n",
       "         '牌': 12992,\n",
       "         '由': 25579,\n",
       "         'H': 2684,\n",
       "         '权': 18302,\n",
       "         '升': 19491,\n",
       "         '泛': 2659,\n",
       "         '费': 19542,\n",
       "         '涵': 1789,\n",
       "         '盖': 5278,\n",
       "         '司': 35598,\n",
       "         '增': 32342,\n",
       "         '半': 13558,\n",
       "         '稀': 917,\n",
       "         '释': 3444,\n",
       "         '尽': 6517,\n",
       "         '管': 33312,\n",
       "         '件': 27489,\n",
       "         '短': 6646,\n",
       "         '振': 3690,\n",
       "         '温': 7706,\n",
       "         '尚': 5108,\n",
       "         '效': 15839,\n",
       "         '独': 7189,\n",
       "         '乐': 16832,\n",
       "         '难': 15897,\n",
       "         '持': 35501,\n",
       "         '变': 18387,\n",
       "         '光': 16183,\n",
       "         '引': 19597,\n",
       "         '热': 14295,\n",
       "         '较': 14654,\n",
       "         '材': 5410,\n",
       "         '明': 39044,\n",
       "         '腾': 2092,\n",
       "         '挪': 1128,\n",
       "         '跷': 78,\n",
       "         '使': 20248,\n",
       "         '得': 50021,\n",
       "         '破': 10307,\n",
       "         '旧': 2958,\n",
       "         '偏': 2289,\n",
       "         '虽': 5074,\n",
       "         '智': 12696,\n",
       "         '无': 27182,\n",
       "         '退': 6264,\n",
       "         '归': 4604,\n",
       "         '登': 8113,\n",
       "         '第': 59215,\n",
       "         '陆': 9140,\n",
       "         '又': 9219,\n",
       "         '怎': 2746,\n",
       "         '秘': 4124,\n",
       "         '悄': 541,\n",
       "         'k': 1440,\n",
       "         '库': 9480,\n",
       "         '配': 9038,\n",
       "         '瞩': 419,\n",
       "         '芯': 899,\n",
       "         '采': 14648,\n",
       "         '艺': 14141,\n",
       "         '八': 7377,\n",
       "         'K': 1420,\n",
       "         'X': 1197,\n",
       "         '销': 10342,\n",
       "         '单': 27329,\n",
       "         '端': 9596,\n",
       "         '签': 7699,\n",
       "         '署': 6321,\n",
       "         '协': 19973,\n",
       "         '议': 29240,\n",
       "         '既': 3301,\n",
       "         '曝': 1041,\n",
       "         '渲': 69,\n",
       "         '染': 4893,\n",
       "         '竞': 10173,\n",
       "         '争': 12143,\n",
       "         '推': 31526,\n",
       "         '球': 103917,\n",
       "         '媒': 16780,\n",
       "         '忠': 2247,\n",
       "         '打': 26321,\n",
       "         '赌': 626,\n",
       "         '座': 6924,\n",
       "         '各': 28699,\n",
       "         '怀': 3556,\n",
       "         '揣': 140,\n",
       "         '沙': 12061,\n",
       "         '漠': 1462,\n",
       "         '雄': 5034,\n",
       "         '鹰': 1280,\n",
       "         '套': 5907,\n",
       "         '牢': 1282,\n",
       "         '筹': 3362,\n",
       "         ',': 12542,\n",
       "         '沪': 1762,\n",
       "         '站': 18434,\n",
       "         '线': 89402,\n",
       "         '黑': 8939,\n",
       "         '鹅': 695,\n",
       "         '拖': 1253,\n",
       "         '累': 4427,\n",
       "         '绝': 4695,\n",
       "         '扬': 3658,\n",
       "         '镳': 33,\n",
       "         '绿': 7227,\n",
       "         '久': 4488,\n",
       "         '策': 15051,\n",
       "         '域': 18458,\n",
       "         '整': 17738,\n",
       "         '压': 6789,\n",
       "         '夯': 294,\n",
       "         '承': 11847,\n",
       "         '告': 19880,\n",
       "         '段': 14224,\n",
       "         '财': 11151,\n",
       "         '闻': 15616,\n",
       "         '央': 10887,\n",
       "         '总': 45664,\n",
       "         '债': 7374,\n",
       "         '境': 20389,\n",
       "         '投': 30226,\n",
       "         '准': 19007,\n",
       "         '务': 52645,\n",
       "         '究': 19277,\n",
       "         '房': 22851,\n",
       "         '《': 36055,\n",
       "         '贷': 5997,\n",
       "         '率': 16433,\n",
       "         '楼': 5508,\n",
       "         '》': 36054,\n",
       "         '污': 5031,\n",
       "         '草': 5744,\n",
       "         '审': 9301,\n",
       "         '五': 15130,\n",
       "         '结': 23579,\n",
       "         '算': 8321,\n",
       "         '展': 80620,\n",
       "         '户': 20792,\n",
       "         '休': 4545,\n",
       "         '眠': 671,\n",
       "         '知': 14914,\n",
       "         '启': 8546,\n",
       "         '账': 3190,\n",
       "         '文': 58174,\n",
       "         '求': 21232,\n",
       "         '六': 6812,\n",
       "         '军': 36948,\n",
       "         '末': 2920,\n",
       "         '额': 8743,\n",
       "         '亿': 19546,\n",
       "         '七': 4837,\n",
       "         '宏': 3146,\n",
       "         '素': 7236,\n",
       "         '鼓': 4001,\n",
       "         '舞': 7389,\n",
       "         '富': 10844,\n",
       "         '际': 49594,\n",
       "         '磊': 1125,\n",
       "         '转': 18095,\n",
       "         '创': 35030,\n",
       "         '；': 25891,\n",
       "         '弱': 2816,\n",
       "         '抛': 723,\n",
       "         '药': 10427,\n",
       "         '造': 29895,\n",
       "         '技': 36699,\n",
       "         '术': 32358,\n",
       "         '临': 8361,\n",
       "         '吐': 1163,\n",
       "         '失': 9380,\n",
       "         '守': 6496,\n",
       "         '留': 8884,\n",
       "         '鸡': 1969,\n",
       "         '毛': 6162,\n",
       "         '每': 19367,\n",
       "         '置': 9541,\n",
       "         '抬': 670,\n",
       "         '即': 9882,\n",
       "         '乏': 1865,\n",
       "         '封': 2730,\n",
       "         '攻': 7113,\n",
       "         '克': 37903,\n",
       "         '却': 5626,\n",
       "         '则': 12008,\n",
       "         '触': 2371,\n",
       "         '疑': 5611,\n",
       "         '负': 13096,\n",
       "         '背': 4881,\n",
       "         '雏': 184,\n",
       "         '旦': 2111,\n",
       "         '味': 4921,\n",
       "         '如': 28248,\n",
       "         '综': 6835,\n",
       "         '脆': 417,\n",
       "         '碎': 739,\n",
       "         '模': 17784,\n",
       "         '糊': 395,\n",
       "         '凌': 2335,\n",
       "         '乱': 2714,\n",
       "         '赚': 741,\n",
       "         '钱': 5117,\n",
       "         '掣': 102,\n",
       "         '肘': 141,\n",
       "         '先': 18898,\n",
       "         '础': 8519,\n",
       "         '弹': 6310,\n",
       "         '积': 16057,\n",
       "         '轮': 20849,\n",
       "         '繁': 3585,\n",
       "         '吃': 4948,\n",
       "         '击': 18136,\n",
       "         '极': 16985,\n",
       "         '似': 3809,\n",
       "         '乎': 3237,\n",
       "         '买': 7573,\n",
       "         '直': 17278,\n",
       "         '吸': 7043,\n",
       "         '莫': 6939,\n",
       "         '齐': 5580,\n",
       "         '闪': 1215,\n",
       "         '崩': 334,\n",
       "         '踩': 286,\n",
       "         '踏': 1411,\n",
       "         '建': 63001,\n",
       "         '终': 11041,\n",
       "         '缺': 4720,\n",
       "         '若': 2524,\n",
       "         '活': 35438,\n",
       "         '跃': 2293,\n",
       "         '战': 43546,\n",
       "         '降': 12594,\n",
       "         '纠': 1471,\n",
       "         '缠': 320,\n",
       "         '促': 8697,\n",
       "         '摸': 1127,\n",
       "         ...})"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokener_counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TextRank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def genrate_name(length=3):  return ''.join(chr(random.randint(65, 73)) for _ in range(length))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AFB'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genrate_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = {\n",
    "    'Yao': ['Guo', 'Wang', 'Tian', 'Tim'] + [genrate_name() for _ in range(6)], \n",
    "    'Guo': ['Li'] + [genrate_name() for _ in range(5)], \n",
    "    'Wang': ['Li_2'] + [genrate_name() for _ in range(5)], \n",
    "    'Li': [genrate_name() for _ in range(3)], \n",
    "    'Li_2': [genrate_name() for _ in range(1)]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Yao': ['Guo',\n",
       "  'Wang',\n",
       "  'Tian',\n",
       "  'Tim',\n",
       "  'BHD',\n",
       "  'DCC',\n",
       "  'GII',\n",
       "  'EEC',\n",
       "  'CIB',\n",
       "  'ECB'],\n",
       " 'Guo': ['Li', 'EAD', 'DAA', 'EII', 'CID', 'FEA'],\n",
       " 'Wang': ['Li_2', 'IAI', 'DGB', 'AEG', 'ECH', 'CGB'],\n",
       " 'Li': ['GEH', 'CIF', 'DBD'],\n",
       " 'Li_2': ['AEB']}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "social_network = networkx.graph.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method add_edges_from in module networkx.classes.graph:\n",
      "\n",
      "add_edges_from(ebunch_to_add, **attr) method of networkx.classes.graph.Graph instance\n",
      "    Add all the edges in ebunch_to_add.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    ebunch_to_add : container of edges\n",
      "        Each edge given in the container will be added to the\n",
      "        graph. The edges must be given as as 2-tuples (u, v) or\n",
      "        3-tuples (u, v, d) where d is a dictionary containing edge data.\n",
      "    attr : keyword arguments, optional\n",
      "        Edge data (or labels or objects) can be assigned using\n",
      "        keyword arguments.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    add_edge : add a single edge\n",
      "    add_weighted_edges_from : convenient way to add weighted edges\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    Adding the same edge twice has no effect but any edge data\n",
      "    will be updated when each duplicate edge is added.\n",
      "    \n",
      "    Edge attributes specified in an ebunch take precedence over\n",
      "    attributes specified via keyword arguments.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> G = nx.Graph()   # or DiGraph, MultiGraph, MultiDiGraph, etc\n",
      "    >>> G.add_edges_from([(0, 1), (1, 2)]) # using a list of edge tuples\n",
      "    >>> e = zip(range(0, 3), range(1, 4))\n",
      "    >>> G.add_edges_from(e) # Add the path graph 0-1-2-3\n",
      "    \n",
      "    Associate data to edges\n",
      "    \n",
      "    >>> G.add_edges_from([(1, 2), (2, 3)], weight=3)\n",
      "    >>> G.add_edges_from([(3, 4), (1, 4)], label='WN2898')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(social_network.add_edges_from)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "for vertex, edge in graph.items():\n",
    "    for v in edge:\n",
    "        social_network.add_edges_from([(vertex, v)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "networkx.draw_networkx(social_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranking_result = networkx.pagerank(social_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Yao', 0.1747698412634544),\n",
       " ('Wang', 0.12616099221117885),\n",
       " ('Guo', 0.12434209154337311),\n",
       " ('Li', 0.07600909782512963),\n",
       " ('Li_2', 0.04007465697366682),\n",
       " ('AEB', 0.022586982681035123),\n",
       " ('GEH', 0.021706946858994478),\n",
       " ('CIF', 0.021706946858994478),\n",
       " ('DBD', 0.021706946858994478),\n",
       " ('IAI', 0.020875426652805028),\n",
       " ('DGB', 0.020875426652805028),\n",
       " ('AEG', 0.020875426652805028),\n",
       " ('ECH', 0.020875426652805028),\n",
       " ('CGB', 0.020875426652805028),\n",
       " ('EAD', 0.020654738077342236),\n",
       " ('DAA', 0.020654738077342236),\n",
       " ('EII', 0.020654738077342236),\n",
       " ('CID', 0.020654738077342236),\n",
       " ('FEA', 0.020654738077342236),\n",
       " ('Tian', 0.020410584159305276),\n",
       " ('Tim', 0.020410584159305276),\n",
       " ('BHD', 0.020410584159305276),\n",
       " ('DCC', 0.020410584159305276),\n",
       " ('GII', 0.020410584159305276),\n",
       " ('EEC', 0.020410584159305276),\n",
       " ('CIB', 0.020410584159305276),\n",
       " ('ECB', 0.020410584159305276)]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(ranking_result.items(), key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Yao': 0.1747698412634544,\n",
       " 'Guo': 0.12434209154337311,\n",
       " 'Wang': 0.12616099221117885,\n",
       " 'Tian': 0.020410584159305276,\n",
       " 'Tim': 0.020410584159305276,\n",
       " 'BHD': 0.020410584159305276,\n",
       " 'DCC': 0.020410584159305276,\n",
       " 'GII': 0.020410584159305276,\n",
       " 'EEC': 0.020410584159305276,\n",
       " 'CIB': 0.020410584159305276,\n",
       " 'ECB': 0.020410584159305276,\n",
       " 'Li': 0.07600909782512963,\n",
       " 'EAD': 0.020654738077342236,\n",
       " 'DAA': 0.020654738077342236,\n",
       " 'EII': 0.020654738077342236,\n",
       " 'CID': 0.020654738077342236,\n",
       " 'FEA': 0.020654738077342236,\n",
       " 'Li_2': 0.04007465697366682,\n",
       " 'IAI': 0.020875426652805028,\n",
       " 'DGB': 0.020875426652805028,\n",
       " 'AEG': 0.020875426652805028,\n",
       " 'ECH': 0.020875426652805028,\n",
       " 'CGB': 0.020875426652805028,\n",
       " 'GEH': 0.021706946858994478,\n",
       " 'CIF': 0.021706946858994478,\n",
       " 'DBD': 0.021706946858994478,\n",
       " 'AEB': 0.022586982681035123}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "networkx.pagerank(social_network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TextRank Page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content</th>\n",
       "      <th>tokenized_content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>此外，自本周（6月12日）起，除小米手机6等15款机型外，其余机型已暂停更新发布（含开发版/...</td>\n",
       "      <td>此外 ， 自 本周 （ 6 月 12 日 ） 起 ， 除 小米 手机 6 等 15 款 机型...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>骁龙835作为唯一通过Windows 10桌面平台认证的ARM处理器，高通强调，不会因为只考...</td>\n",
       "      <td>骁龙 835 作为 唯一 通过 Windows   10 桌面 平台 认证 的 ARM 处理...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>此前的一加3T搭载的是3400mAh电池，DashCharge快充规格为5V/4A。\\r\\n...</td>\n",
       "      <td>此前 的 一加 3T 搭载 的 是 3400mAh 电池 ， DashCharge 快充 规...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>这是6月18日在葡萄牙中部大佩德罗冈地区拍摄的被森林大火烧毁的汽车。新华社记者张立云摄\\r\\n</td>\n",
       "      <td>这是 6 月 18 日 在 葡萄牙 中部 大 佩德罗 冈 地区 拍摄 的 被 森林 大火 烧...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>（原标题：44岁女子跑深圳约会网友被拒，暴雨中裸身奔走……）\\r\\n@深圳交警微博称：昨日清...</td>\n",
       "      <td>（ 原 标题 ： 44 岁 女子 跑 深圳 约会 网友 被 拒 ， 暴雨 中 裸身 奔走 …...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             content  \\\n",
       "0  此外，自本周（6月12日）起，除小米手机6等15款机型外，其余机型已暂停更新发布（含开发版/...   \n",
       "1  骁龙835作为唯一通过Windows 10桌面平台认证的ARM处理器，高通强调，不会因为只考...   \n",
       "2  此前的一加3T搭载的是3400mAh电池，DashCharge快充规格为5V/4A。\\r\\n...   \n",
       "3    这是6月18日在葡萄牙中部大佩德罗冈地区拍摄的被森林大火烧毁的汽车。新华社记者张立云摄\\r\\n   \n",
       "4  （原标题：44岁女子跑深圳约会网友被拒，暴雨中裸身奔走……）\\r\\n@深圳交警微博称：昨日清...   \n",
       "\n",
       "                                   tokenized_content  \n",
       "0  此外 ， 自 本周 （ 6 月 12 日 ） 起 ， 除 小米 手机 6 等 15 款 机型...  \n",
       "1  骁龙 835 作为 唯一 通过 Windows   10 桌面 平台 认证 的 ARM 处理...  \n",
       "2  此前 的 一加 3T 搭载 的 是 3400mAh 电池 ， DashCharge 快充 规...  \n",
       "3  这是 6 月 18 日 在 葡萄牙 中部 大 佩德罗 冈 地区 拍摄 的 被 森林 大火 烧...  \n",
       "4  （ 原 标题 ： 44 岁 女子 跑 深圳 约会 网友 被 拒 ， 暴雨 中 裸身 奔走 …...  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pure_content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "lengthes_of_text = map(len, pure_content['content'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[177,\n",
       " 329,\n",
       " 216,\n",
       " 45,\n",
       " 1674,\n",
       " 1056,\n",
       " 657,\n",
       " 4093,\n",
       " 3665,\n",
       " 755,\n",
       " 169,\n",
       " 1467,\n",
       " 487,\n",
       " 774,\n",
       " 1137,\n",
       " 331,\n",
       " 293,\n",
       " 495,\n",
       " 289,\n",
       " 1707]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(lengthes_of_text)[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/anaconda3/envs/ibm/lib/python3.6/site-packages/networkx']"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "networkx.__path__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.font_manager as fm\n",
    "fp1 = fm.FontProperties(fname=\"/Users/mqgao/Downloads/SourceHanSerifSC-Regular.otf\")\n",
    "# how to set chinese display: \n",
    "# https://knowlab.wordpress.com/2016/05/25/networkx-%E7%B9%AA%E5%9C%96%E9%A1%AF%E7%A4%BA%E4%B8%AD%E6%96%87%E7%9A%84%E8%A7%A3%E6%B1%BA%E6%96%B9%E6%B3%95/\n",
    "\n",
    "def get_connect_graph_by_text_rank(tokenized_text: str, window=3):\n",
    "    keywords_graph = networkx.Graph()\n",
    "    tokeners = tokenized_text.split()\n",
    "    for ii, t in enumerate(tokeners):\n",
    "        word_tuples = [(tokeners[connect], t) \n",
    "                       for connect in range(ii-window, ii+window+1) \n",
    "                       if connect >= 0 and connect < len(tokeners)]\n",
    "        keywords_graph.add_edges_from(word_tuples)\n",
    "\n",
    "    return keywords_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_content = pure_content.iloc[1]['tokenized_content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_content_continous = pure_content.iloc[4]['content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['骁龙835作为唯一通过Windows',\n",
       " '10桌面平台认证的ARM处理器，高通强调，不会因为只考虑性能而去屏蔽掉小核心。相反，他们正联手微软，找到一种适合桌面平台的、兼顾性能和功耗的完美方案。',\n",
       " '报道称，微软已经拿到了一些新的源码，以便Windows',\n",
       " '10更好地理解big.little架构。',\n",
       " '资料显示，骁龙835作为一款集成了CPU、GPU、基带、蓝牙/Wi-Fi的SoC，比传统的Wintel方案可以节省至少30%的PCB空间。',\n",
       " '按计划，今年Q4，华硕、惠普、联想将首发骁龙835',\n",
       " 'Win10电脑，预计均是二合一形态的产品。',\n",
       " '当然，高通骁龙只是个开始，未来也许还能见到三星Exynos、联发科、华为麒麟、小米澎湃等进入Windows',\n",
       " '10桌面平台。']"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_content_continous.replace(',', ' ').split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "pattern = re.compile('[。，,.]')\n",
    "token_by_sentence_first = pattern.sub(' ', first_content_continous).split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['（原标题：44岁女子跑深圳约会网友被拒',\n",
       " '暴雨中裸身奔走……）',\n",
       " '@深圳交警微博称：昨日清晨交警发现有一女子赤裸上身',\n",
       " '行走在南坪快速上',\n",
       " '期间还起了轻生年头',\n",
       " '一辅警发现后赶紧为其披上黄衣',\n",
       " '并一路劝说她',\n",
       " '那么事发时',\n",
       " '到底都发生了些什么呢？',\n",
       " '南都记者带您一起还原现场']"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_by_sentence_first[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence_graph = get_connect_graph_by_text_rank()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/ibm/lib/python3.6/site-packages/matplotlib/font_manager.py:1328: UserWarning: findfont: Font family ['/Users/mqgao/Downloads/SourceHanSerifSC-Regular.otf'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFCCAYAAADL3BUJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3V+MFed5x/HfnD3LnnNaiXJh7LTF9MZKUTDYKotsX9RO0qQlYJPd5iJSjBSuKhJoL1pHwm2N3UghILdpa+rtXVAkq7kw/2ILN7GcyFFVxxxaYzBpa98kOFXsg1RkKWW9sDC9eDthWM6eM3/eeeedme9HWtnYO3NmD2fnmfd5n/d5gzAMQwEAAG+0yr4AAABwM4IzAACeITgDAOAZgjMAAJ4hOAMA4BmCMwAAniE4AwDgGYIzAACeITgDAOAZgjMAAJ4hOAMA4BmCMwAAniE4AwDgGYIzAACeITgDAOAZgjMAAJ4hOAMA4Jl22RcAAKi4wUA6fFg6e1b64ANp5UppwwZp507pttvKvrpKCsIwDMu+CABABfX70v790ksvmT9/+OGN/9ftSmEobdki7d0rTU+Xc40VRXAGACQXjZKff17693+Xrl0b/f1BYAL1009Lu3Y5ucQ6IK0NABgvPkpeXDRfSYShdPmy9Gd/Zv5MgE6EkTMANN24OeO5ORNc5+dNsM2q15NefVXatMnapdcVwRkAmirJnPFHPyr913/d/P+yCgJpZkY6ciT/uWqO4AwATWRrNJxWpyNduEAV9xiscwaApokC8+XLbgOzZEbPhw+7fc0KoiAMAJqk35f+9E/NiLkM8/PSuXPlvHaFMHIGgKbo96Xt28sLzJFLl8p9/QogOANAE8zNSQ8+KP3852VfiXT+vHTwoHTxYtlX4i0KwgCg7uJzzL6gg9hIBGcAqKvBQPrqV01wHtfJqyx0EBuKgjAAqJv4+uUrV6Tr18u+ouXRQWwoRs4AUCdlrV+2gQ5iv0RBGADURZnrl22YnzcjfjByBoDKivfE/ulPpdde83duOSk6iElizhkAqmdUT+yqizqIPfZY2VdSKoIzAFRJleeUk6CDmCTmnAGgOsqcU56aMltJukAHMYIzAFRCv19eI5FuV/rGN6SXXzb/XrRVq4p/Dc8RnAHAd4OBtHNnOYH5139d+uEPzfrj6Wnpr//aLHkqSrcr3X13ceevCKq1AcBXUeHXyZPSwoL71+92TWBeuu64yHagVGtLYuQMAH6am5Meekg6frycwNzrmVHysIYgu3aZZiFr1th9zSCQPvOZxgdmieAMAP4ps/ArCExgHtfretMmaeNGu6/dbptNMMBSKgDwSlmFX62WtGKFGbnu3Zushabt6u0gsHu+CmPkDAA+2b/frPV1aWJC+vKXzVzvkSPJe1tv2GDmiG25epX2nf+PgjAA8MVgIN15p9s55iQp7OUMBtLatXY7lLVa0l/8hbR7d6Pnnhk5A4AP+n3pE59wF5iTzi2Psnq1tGWL3XT09evS175mHlJmZ8370kCMnAGgbEUuTVqq2zVFZmnmlkfp901VeRHXHgTmevM8QFQUwRkAyuQiME9MSA88IP3Wb5kGH1/8ot2UcdE/Q94RfgURnAGgLEWOOiOuAtvcnLRnT3FbVvZ6Zm113pF+RRCcAcCl+B7Mr7wivfdeMa9TRkr4j/9Y+od/MPPGRZidNdXkDUBwBgAXXO7BPDUlbd1qZ045jSKqt+NaLfP+ffrTxZzfIwRnACiayz2Y16+Xvv/98pYhzc6alqNF/ZyTk9Lf/V3t558JzgBQJJeV2D7My9ZpHr1ErHMGgKK4bMUZBayyC6amp811FLmt5OXL5n09fbq41ygZwRkAiuKqFadvI8ldu24E6KL6Zc/P17rVJ2ltAChC0cVRkfXrpW9+s/wR8zCnT9/Yj3px0XzZNDUlvftuLdt8MnIGgCJ89avSlSvFvsbUlCn+8jEwS+a6jhwxG2o8/riptrZpYUH6+Mdr2eKT4AwAts3Nma+i1vtKJl28dWs1Ro233SY99ZS0fbv9NPf586YAbW7O7nlLRnAGAJui6uyiOmVFul2zjrlK9u41121bVCBWowBNcAYAW1xVZ/tSmZ1WkZXcNavgpiAMAPJw1Y5Tqs8uTXNzptWn7QKxIJBmZmrR4pPgDABZuGzHaXubRx9873tmL2jb8/KtlgnQmzdLO3dWY05+CIIzAKTlqh3nHXdIn/pUMds8+qDIVp/RA82WLeaBZnra/msUiOAMAGm4asfpQyvOorlo9VnRqQAKwgAgKQq+7HLR6jMMK1nNzcgZAEZxWfAlSRMT0jPPVGqUlxvZiFsQnAFgGJcFX5FWS/ryl6W///viX8s3p0+bAq633iruNSpUzU1wBoClXO6/HNfpmFaXdSv8SmowkO6807TlLEpF3mPmnAEgLp5idRmYg8AslfI8aBRq9WrzHhS1k5Vkzn34cHHnt4TgDAARl/svL1XFdpxFKKrFZ2R+Xjp3rrjzW0JwBoCIq/2Xl2pKdXYSLiq4L10q7tyWtMu+AADwwvnz0gsvuE9lV3ANbuGi96Koef/BwO75CsDIGUCz9fumU9XGjfZ7PS+n2zWFSTMzZmkPgflWu3aZ92ZmxrxXNlPdb7zh/QYZVGsDaC7XVdl1b8dZlIsXTRHXqVPS0aP5+3FXYEkVwRlAM7lqfBGpUAMMr83OSseO5T/P1JT07rvePiCR1gbQPK6rsin4smfvXqltoVxqYUH6+MfNZ8FDjJwB1Fu8/eYHH0grV0pnzkg//nHxqWwKvoqxebO9oBo9OHn290O1NoB6KqP9ZqSO+y/75Pbb7Z0r2hRD8ipAE5wB1E9Z7Tcp+HJj5Uq754sC9PS0Nw9SBGcA9eK60CvS65l10p7c3GttwwZTaW0zGzI/bzItnlRwM+cMoD76femhh8oJzB7OW9bWYCCtXWt/qsKjCm6qtQHUh+v2m0FAYC7D6tXSli32N8jwqIKb4Ayg+gYD6YknpBMn3M0xt9t0+CpTURtknD9vsi9zc/bPnQJp7bhhSy42bDAbgMfTHEm/D0Cx4hXZV69K1665ed2JCbOz0bp1bl4PwxVZX1ByRoTgLI1echEtidiyRdq+3TyZj/u+vXtN1R+A4pRVkV2B1o+NUnSALqmrG8E57S94EIz+PpoOAMUrqyJbog2nj06fNpnLt96ye94SH8SaHZxrnBIBaqusimyJ32ufDQbSnXeaoi6bOh3pwgXnU5bNDc4ufsF5wgbyW1rjceaM9N//zb7LuNXsrHT8uN3PRrcrPfWU9Nhj9s6ZQHObkLhYcrF0UTuFZEByZbbfjNCGs1r27pW++127g675eVP851gzR85FLWAfptMxRWT/+I8UkgFJlVXstX69dO+90qVL0qpVtOGsoiKmK7dtM93fHGpmcD54UNq3z01wbrdNSmxxkUIyIIky228yDVUPtj9DmzdLr79u51wJNbMJydmz7lJki4tm/eW4Z6AwvNF8veTF70BpXO+zHGG/5XrZtcs8aK1fb+d8b7xhKsIdaubI+eGHpRdfLPsqltdqSZ/+tGkjx3w06qrMfZYjZKzqzVYFdwlLqpoZnB99VHruubKvYjzmo1FHPhR6tdvmi0Kv+pudlY4dy38ex0uqmpnW3rDBvNG+m583N65jx6QHHjDzHg8/bB4uDh6ULl4s+wqBdObmzBLG48fNZ7uMwNxqSX/+5+ZGe+QIgbnu9u41D2J5BYHJ9DjSzJGzy2rtojCqRtWU2dUrQuvNZtq82c5OUzt2SN/6Vv7zJNDMkXNR2425FI2qjx/3YgcVYKSyCr2W6nbNwyya5fbb7Zzn0iU750mguU1IilisXoZ4lbd0o6iFhifwiet9loehIru5Vq60c55Vq+ycJ4HmBufpafOL6sPTvA1RgO71lt856+hRs76bVDhcGgzM57GsGTQqsrFhg5nKyDuV+Wu/Zud6EmjmnHNcWZ2IijIxIV2/Pr7hSacj/f7vS7/yK4ysUawnnjAj58VFt69L601EbNUZbd3qbBkuwVkyi8v375dOnjSBK55+63alK1fML/n16+VdowujisxIkyOtaMnUiRPufnfWrJE2bqT1Jm61dau5x+fhcDkVwTnu4kUTgM6du7m37tat0u/8TrWru9OIpwE3bVp+TSoV41hOGRkp2m9ilD17pEOH8p3D4Q5VBOekitiKzHeTkyZQj2s/ypxecyTJoJSxZIp9ljGOreZTjpZTEZyTKnOD96qI3yBJg9fLqK5e8QzK9u3Sl77k7veEB0MkZatts6MdqgjOaWQZESQdfdbF1JR0//3Sj35k/pw0DU4w91fSFHUQmK8wLP6zTrEX0mLkXHNpblTRE/309PIFZ01ka06bgF48H7p6SeyzjPwOHjT3kTzFia2W9PWvM+fsrXHV3cs90UcFZz/4gfS970nXrjm/dK9kndNOmmKlSC0fX6ZyKPSCDefP29lC8sc/ltaty3+eMQjOeSxX3Z3kid6XEUmV9HrSI49I3/lOuswFc5Hj+bB94zAUesGWio2cm9shzIbbbsv+lxTdbOrUAKVoly9L3/52su9drq0pbubD9o3D8HAF286ezb/e/vp1MxhzgJFz2UalyGHHsLRolvnqKs1xp1ny5NvDYaslffazFHrBrrvvlt56K/95HFVrK4QfBoMwPHgwDHfsCMNt28Jw8+YwnJwMwyCIal+HfwVBGLZa47+vyV9BEIazs+Z9PnUqDGdmwrDTMV/x7+t2zX+bmTHfF8lyTFmSXutXvhKGvV75fzdLv9rtMHziibLfRdTNs8+G4cSEnc/ojh1OLpmRs8+SFp498ojbtaVV1OlIf/VX0pNPppuvltJX5w9Lw2Yddac5zteRcBoO2yOiIWwWNjrsECYnjwDIZ+moescO8+fB4Mb3PPusnyMhX77abZOJSHPM5GT6Y3o983cRyTrqTntcHf7+4xkOwJaZGXuZxU7n5vtugRg510kdRk51EM1x9/vZRt1p19Lv3m16Blc9c8KSKdhmazcqyfy+zcyYrScdIDjXDQVm5QsCszPS22+nC5jxpWJpjmu1bjzbVxVLplCEgwfNlqULC/nP5fjhkeBcV8PWYP/iF9I//7N5ihw3Imu3zT+vXHF2yaiZpHuLs2QKRej3zYZFP/tZ/nNNTEjPPOP0M0pwbpo03c2SpmWBpXo9k54/cSJ9Jz0gL9tNntavd7a+OUJwbqqk3c2SBPP77jMbXfjSwALlWpqiztNJD0iriO6Ljja7iCM4I5lxN1jakYIUNcpWRD94l8unYgjOsCdNlTFz2vVBihq+mJ2Vjh+3Ow1X0tp7gjPsKmJOOxqRZalkjh4Crl7N/jPhZmzfCB+dP29WSdjc7c/x8qmbXprgjELYnNOOj8jSrgHet898MR9uB2uR4Zto85YXXpAWF+2eu8TPO8EZfkhTNJQ2oBeR6moi1iLDN0U2Xir5805wRnUlDehFFInUSRCwFhnVU2QRqgcPogRnNEOWX+TJyfRFa72edNddZqMK179aExPp5tt6PWnPHumdd1iLjGop8oF7zRrp6NHSP+/tUl8dcCV6Ak7b6zrLMZs2uR+pR4H2mWey7aDFWmRUyf79xbQm7nTM75EPD6JOttcAfNHvm52POh2zu9Ow3Z5mZ8335Tkmyy5RvV4Yfv7z2Y6LdsLKcq1Albz//q07tdn6crjr1DiktdFMWUaKaY9JW1medVeqYXNjjIRRN9He5t/+tvTmm6Zvu00lLpsahuAMFCltZXne44C6iZZKvfSS+XNRyyI9WyZIcAZcyDqSZQSMJnO1R70H1dlLEZwBAP5x0a/f42WCBGcAgF9c9CaYmJC2b/d2aoilVADcigp7zp6VPvhAWrlS2rBB2rlzdKo+63GonqKWSkXabfM5WreuuNfIiZEz0GQuA+Wowp6oyG3LFjOSmZ7Of5yNnzPvsUgm/h4PBtIrr9ivxo54VpW9rHJWcAEo1alTYTgzY9Z1Ll0zGq2Hnpkx32fjuGjddxCMXmcaBDev2856XN7rzXsskhn1Hhf11etVYp0/wRloGteBsqyGLHkCe96Hgsj774fhgQNh+IUvhOG2beafBw540+iiVEnfY9uBebm/K8+Q1gZ8U2SqOUsFbK+XbS/tXk/avVs6dKi8VqZprzfesjXLsVHFr41UfJ25qMSO87gqezkEZ8AXRc/Jbt8ufelLbgNlq3Vj3OJS2k1AIlNT5kaepdFF1MSi38/f4a3OXO4SV+GGPQRnwAcuWn1GW0PyK1+MIJA2bpTefjvfqFuqRxHacj/Dq6+aB8kiP4etlnTPPdLnP1/Zhj0EZ6Bo4260LlPN8FM06g7D6qfDR2VyOp3i2m/GdTrShQuVDMoRgjNQlCTp5vvuk370Izc3LPgrPuqucjrcVbvNUaqyVGoMgjNQBB9uUqg339Lhrou8luPZBhZZEZyBPIbdDP/3f6XvfrfYDkeA5E863GWR1ygebmCRFcEZyMLVNnbAKL6kw2dnpePHy01l+5rqz4jgDKRFyhpVZnt0ORhIa9eW84Ba4aVS47DxBTDMcnN3QSA9+WT56Tsgq8uXzcPl9LQJZknnqZf7vl/8wu31t1rSJz8p3XFHrfc2Z+QMxI1KV09NSQsL5VxXlWVtCJJVWY1PqiQIpAcflFatSta85sSJ5b9vYaG4TSqGXXcNKrGTIDgDEdLV9uVppVlGy9A8HcKqKGpMk/X/u1aTSuwkWmVfAOCF+DIQn25GPuj1TKelXi/9cU8/LX396+afvZ652Y8SBDeO+6d/ynbcgQM3jkt7vd/4hvQ3f5Pt2I0bx1+nb8Z91n36XYj+fhsQmCWJXamAU6fS735U9a+JCffbN/b7YTg7a7YG7HZvPibagnF29tbt/LIe53pXqiZ+jlx8jdv5q6ZIa6PZBgPpE5+Qzp8v+0rc6fVMpuDECenkSTPai6/JHlUBe/q0mZNPe1zcxYumsOjcOenSJTPvmaSwJ8txea43y7G+NOKooqWtPWtciZ0EwRnNFBV+nTxZzSKvPHOy8WU0LgNlmfJcb9pj025ictddpgK6qbfiIDAB+MEHq/N5coDgjOapcuFXnl2patakwWtpRt1h6Ed3rbI0qMgrDYIzmsXntOPUlPTAA9Jrr7lPNaMYSUfdPn8ui1Sjdpu2EZzRHL70/x0mfpNqSqoZN2tSOpxMzlgEZzRH2f1/h+Emhbi06fDf/d1qrclu/f/q3d/4Demzn5X+8i95cFwGwRnNMBhIa9ZIV66UfSUG6WaMkiQLMjcn/cmfSFevlnml2bnYLavCCM5ohpkZM2ouQ7cr/cEfSL/6q6SbYUed5qjJHg1FcEb9zc2ZFpIu+ztL3HSQXpJNKHyunciD4rCbEJxRby5uZEs3xCBljbRGbbiyNP27f79/tRO2sKzqlwjOqLcii8B6PWnfPjNCpkIaWaWp0u50zBzz4qK763OpQbtOjUNwRn0VtQk86WrYUqe5Y1taLen3fs883A7bV7ohCM6or4MHzcjWZnCempK2biVdjdHqNnccLYFytW9zpMEV3QRn1Nejj0rPPWfvfLffLv3gB9K6dfbOiXqp49xxlGr+oz+Stm0rZ+lWA7NVBGfU18MPSy++aO98U1PmJtHAp3gkUNe543iRVtlp+AZVdBOcUV+2R86RBj7FN1aS9LRUftAqyrBgWPbGMQ2p6CY4o76KmHOOa9BTfOOkSU9L1Zk7TmrcA+ioNqMurFkjbdy4/MNSDRCcUV9FVWvHTU5Kn/yk1G7X+kZReUlHwFK9N6AIAvMgsWqVnV3MlrYZ7XTMPLrLdH1Ni8YIzqi32Vnp2DF3r7fcjSJNcMBwWd7DNCPg6en6pqcj8ZRwUbuYlbXBTM2mmwjOqLd+X7r//vJad+7eLb3zTvLgECGY35A2wEbSjoB375YOHap3YHYRuMpeIlaT6SaCM+pv82Zzw/BV/Il/06ZsgSiPrA8CLo5LG2Cjm3KWEfDEhPuHuDzabTOt8uGH6d4bF8rOQNShaCwE6u7AgTBst8PQ3ML8/VqxIgwnJ8MwCEZ/XxCEYa8Xhs8+e/PP+f775mf9whfCcNs2888DB8JwMBj+vpw6FYYzM2HY6Ziv+Gt0u+a/zcyY7yvjuGefNT9nmvew1wvDr3wl/XFV+wqCMJydDcN+3/yz0zHv4bD3NPo+16K/v3Gf5yLfnwpT2RcAFO79928NBnX4igJ0lmCZ9Ma59EHA1XF5AuzERPl/Ny7+7uMBdzAIw4MHw3DHDvNgtmOH+fNyD2aujHp4KPqr0yn/58+BtDaaoawilaKtWGHSlleujP7Z4qlNKX3KsdeTHnlE+s533BxXtRSzS1WcU40Xn733nvTKK8W3Am23pccfl556qtjXKQjBGc1QdpGKLzodE/TKaMGI4XyeOy6Kq4flVkvavr2SS6xaZV8A4MT0tLmp9XplX0m5PvyQwOyTIDCZhR/+0PSv7nRMAI7rds1/n5kxRU5VD8ySCZZLf84iXL9uHgIeesgUqVUII2c0S9mtB4G4pVXFRa099pHriu6KTQcQnNE8ZbceBKTKBYtCuH5YrtASK4Izmis+SnnjDek//oMiJGQTBKbX89tvp1+T3XQuH5aj7S+PHCnuNSwhOAMRUt7IKhqRScsHmrR9q5tmaUr/zTeln/3M/u/i1JT07rveTxMQnIE4Ut7NFgTmK80yn2Hp6SbNHRelyBUWH/uY9M1vel3BTXAGhll6c712TXr5Zbe77SCbLAE20utJe/ZIzzxDetoHRRaNeT7nT3AGkiq7XzCSiQfYtA1Topv1qAwK6Wm3ipxu8jhAE5yBNNJsxMCvVj55U8xZN82IIz3th+hh6fhx+53FPK3gJjgDaSUdVd11V/L06OSk+R4ahNxgI8XMCLhe9u2TvvY1u9NLnlZwE5yBrJKMqtIEh34/+Uhvaip7G86oH/fCQvJjXPfWtp1iZgRcD4OBtHbtzVup2tDpSBcuePVZIDgDLiQNDlmCeZagJ+XbJ9nVcXEEWEjF9OXuds0GGY89Zu+cORGcAR8lDUR5gl7WEanr44C4opZY7dghfetbds+ZA8EZqLq8QS/riNT1cUCkiJUT27ZJL7xg73w5EZyBuiDooUlsB+jNm6XXX7dzLgsIzgCAajp9Wtq5U3rrrfznmpyU/vVfvZlSITgDAKprMJDuvDPd6oNhPFtS1Sr7AgAAyGz1alNTkVcYmrqNixfzn8sCgjMAoNr27pXa7fznCQJTt+EBgjMAoNqmp6V7781/nvl5U1DpAYIzAKD6br/dznkuXbJznpwIzgCA6lu50s55Vq2yc56cCM4AgOrbsMH0jc9jasr0BvAAS6kAANU3GEgf+Ui+LSVbLem997xo2sPIGQAAzxCcAQDVd/iwGfnmMTnJUioAAKx5/nlpcTHfORYWWEoFAIAVc3PSv/2bnXOxlAoAgJz6fbM7VZ5CsDiWUgEAkNP+/TfvYZ5Ht8tSKgAAchkMpLVrpQ8/tHO+Tke6cIGlVAAAZHb4sL10dhCY3a08CMwSwRkAUFXPPy9duWLnXN2u2d3KEwRnAED12KzQbrWkp5+WNm2ycz4LmHMGAFRLvy899JB0+bKd823eLL3+up1zWcLIGQBQLTYrtKempM99zs65LGLkDACojvPnpY0bpWvX7JzPowrtOEbOAAD/9fvS7Kx0zz32ArNnFdpxjJwBAH6bmzNdwObnJZshq9eTXn3Vq0KwSLvsCwAAYFlRYLZV/BXxsEI7jpEzAMBPtquy4zys0I5j5AwA8MdgYDp/nT0rvfJKMYHZ0wrtOEbOAIDy9ftmidRLL5k/2+qXPYynFdpxjJwBAOUqquBrGI8rtOMYOQMAylNUwddyPK7QjmOdMwCgHP2++8DscYV2HGltAEA5bLbhHCUIzK5TTz8t7dpV/OtZQHAGALg1GEiHDkknThQ/xzwxIW3fbraDrMCIOUJwBgC4Ea/IXlyUrl8v9vXabbMka926Yl+nAARnAEDxXFZkSyaV/cgjlQzMEtXaAICiua7IlipTlb0cRs4AgPzinb0++EBauVLasMHsIlVGYK5IVfZyGDkDALIb1dmr25UWFoqfW45UsCp7OQRnAEA2rueRl9Ptmtf/zGcqV5W9HNLaAID0yphHXuqOO6RPfUq6+27pi1/0viVnGoycAQDpFLmVY1IVL/gah5EzAGC0pcVeb75ZfmCueMHXOARnAMBwLrdxTKJGBV/jEJwBALfypdhLMp2+2u1aFXyNQ3AGANzMh2KvSKslPf64tHt3rQq+xqEgDABwgw/FXpEgkGZmpCNHyr4S5xg5A0CT+VbsFdftmjR2AxGcAaCJfCv2WqoBFdmjEJwBoGl8KvZaqkEV2aMQnAGgSXwo9mq1pKkp83AQqWELzjwoCAOAOllud6idO6Wf/KT8Yq9eTzp+XDpzRjp3Trp0SVq1qpYtOPMgOANAHYzbHSoMTRD8+c/LuT7pxjxyg9PVSRGcAaDqfJ5DlphHzoA5ZwCoMh/mkJfDPHJmjJwBoKp8ahgimRHyb/6mtHEj88g5MXIGgCoYVuh15ow/gVkyI+WjRxkhW0BwBgCf+d4sJNLwpiG2EZwBwFe+F3pJFHsVhOAMAD7yudBLotirYBSEAYBvfCv0kij2coyRMwCUqQqFXhLFXo4RnAGgDFUp9JIo9ioBwRkAXKtCoZdEsVeJCM4AYNOojSduu82fQq8gkD7yEel//sf8OztEeYWCMACwIcnGE/fdJ732mrSwUM41xvV60quvSmvXmocJdojyCsEZAPKqSpo6wu5Q3iOtDQB5+JKmToI55Mpg5AwAWfm4HnkY5pArh5EzACRRlfXIQSB97GPSvfcyh1xhjJwBYJQqrUeWbhR6MTquNEbOAJpn3HKnSFULvQjMlcfIGf5LeiP1/TVQviTLnbZsMfOyp09T6IXSEJzhrzQ30ulpf18Dfkg6Cg4CaWpKunZNunrV3fVlQaFXbRGc4ac0N9JxI4blRsWS9NRTdl4D5apKV648KPRqFIIz/JPlRjqsqcKoUfHkZPpREY0b/FO1rlx5UOjVKARn+CXPutH4zauoQp4kN0jmr92oWrFWHjwYNg7BGX6ZnZWOH89+s73jDmnNGumNN6QUjSZaAAADf0lEQVTFRbvXJpnU4syMdOTIrf+P+Wt36pCmToIplcYiOMMfg4Fpwu/7OtJWywTozZtvndNMM3/9h3/YnBF2lmzCcsfcc495/6semDsd6f77TcqdXaGwBMEZ/jh4UNq3z//gHIluoB/9qPSf/5luTnNiwtyQ2+16j7CzZBPGHbOwIF2/7ub6ixJPU1+8yK5QuAXBGf549FHpuefKvgo/5E1nFj3vneT8WbIJUr3nkUlTIyGCM/zx8MPSiy+WfRV+SVsIlHXeO2kwT3r+u+6SDh1Kl3pescIc7/va4ixIUyMlgjP8wch5uF5POnbMbLJge6S6aVP6jllJzt/k2wrrkWEBwRn+qNqcs0utlhlZFjFSXVwcH2wnJ+s7qrWN9ciwgOAMf1SlWts3TR+p+oT1yLCkVfYFAL+0erUZBQZB2VdSLQTm4qxYYfpsj/tMBgGBGVYRnOGXvXtNuhYoW68n/e3fSv/yL2Zddadz62ez2zX/fWbGpLIJzLCEtDb805TuT/DTcsudWI8MhwjO8FOT+ibDHbpyoSIIzvDX6dNmmc/Jk7feSIvQbhfTjxt+oCsXKoTgDP/Fb6Qvvyy99569c0cpzCefNH8+dUo6erT67SFxA125UEEEZ1RLni0l40alMPPujAX3Wi1TVU2aGjVBcEb1ZCkYm5w0HZtWrx6fwrT1AAA3ej3zMHXmDGlq1AbBGdWUpVVlmpRmlgeATkf67d82O1QNKza6etWky0mZD5eltzZri1FTBGdU16iCMRspzawPAKOKjZ5/nir0pbLsSsU8MmqO4IzqK7LytogHgHHnrMN+xZIZ1e7ZI73zTrr3r+iHLqACCM5AEkU8ACx3zo0bTcepoua8s25kkWajjKWj2izvH8ud0GAEZ8BHWea8045U+/30KeTpaUa1gAMEZ8BXeYreko46s6aQGdUChSI4Az5zNf9KsAW8QnAGqoDgCTQKwRkAAM+wnzMAAJ4hOAMA4BmCMwAAniE4AwDgGYIzAACeITgDAOAZgjMAAJ4hOAMA4BmCMwAAniE4AwDgGYIzAACeITgDAOAZgjMAAJ4hOAMA4BmCMwAAniE4AwDgGYIzAACeITgDAOAZgjMAAJ4hOAMA4BmCMwAAniE4AwDgGYIzAACeITgDAOAZgjMAAJ4hOAMA4BmCMwAAniE4AwDgGYIzAACeITgDAOCZ/wMSbPYcMjiiYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "networkx.draw(sentence_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranking_sentence = networkx.pagerank(sentence_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'（原标题：44岁女子跑深圳约会网友被拒，暴雨中裸身奔走……）\\r\\n@深圳交警微博称：昨日清晨交警发现有一女子赤裸上身，行走在南坪快速上，期间还起了轻生年头，一辅警发现后赶紧为其披上黄衣，并一路劝说她。\\r\\n那么事发时\\r\\n到底都发生了些什么呢？\\r\\n南都记者带您一起还原现场\\r\\n南都记者在龙岗大队坂田中队见到了辅警刘青（发现女生的辅警），一位外表高大帅气，说话略带些腼腆的90后青年。\\r\\n刘青介绍，6月16日早上7时36分，他正在环城南路附近值勤，接到中队关于一位女子裸身进入机动车可能有危险的警情，随后骑着小铁骑开始沿路寻找，大概花了十多分钟在南坪大道坂田出口往龙岗方向的逆行辅道上发现该女子。\\r\\n女子身上一丝不挂地逆车流而行，时走时停，时坐时躺，险象环生。刘青停好小铁骑，和另外一名巡防员追了上去，发现女子的情绪很低落，话不多，刘青尝试和女子交流，劝说女子离开，可女子并不愿意接受，继续缓慢地往南坪快速路的主干道上走去。\\r\\n此时路边上已经聚集了很市民围观，为了不刺激女子的情绪，刘青和巡防员一边盯着女子一边驱赶着围观的群众。\\r\\n现场还原\\r\\n从警方提供的一份视频了解到，16日早上7时25分，女子出现在坂雪岗大道与环城南路的监控视频中，此时女子还穿着白色的内裤，正沿着坂雪岗大道往南坪快速的方向缓慢地走着。\\r\\n当时正值上班高峰期，十字路口的车流已经排起了长队。当女子出现时，路上的市民纷纷驻足观望，不少车辆也放慢了速度，但女子并不为市民观望停下脚步，依然缓慢走着。当女子行进到十字路口中间时，一辆大货车挡住了镜头，但是当女子再次出现镜头时，可以发现女子已经没穿内裤了，全身裸露继续朝着南坪快速方向走去。记者发现，视频中女子周围并没有人尾随或者上前劝止的市民。\\r\\n一大清早路上看到这样的情况\\r\\n恐怕大家都没办法淡定\\r\\n面对这一情况\\r\\n刘青表示，“一开始根本不敢看她，心里挺别扭，感觉很尴尬”，但当刘青跟随女子上了南坪快速路主干道时，女子作出了让人意想不到的举动，她突然靠近护栏要从上面跳下去，刘青赶忙冲上去拉住了女子的手，将其控制住并远离护栏。碍于女子没有穿衣服，刘青递上衣服，女子没接受还把衣服扔到排水沟里，继续往前走，没办法刘青只能紧紧拉着她的一只手跟在后面。\\r\\n刘青一路上耐心地开导安慰她，但只听到她不断地重复着一句话“要是你也遭遇我的事，你也会这样的”，期间她还不时试图挣脱刘青的手要冲向护栏往下跳。\\r\\n就这样，我被牵着走了大概十多分钟，天突然下起了大暴雨，雨大的连眼睛都睁不开”刘青继续说着，瞬间他们就被雨透了，但女子依然不愿意接受刘青的帮助，就继续冒着大雨往前走。\\r\\n大概走了有四十分钟吧，女子突然停下来说“我想回家了”，然后女子也接受了刘青递过来的小黄衣，就出现了深圳微博上的照片，女子披着小黄衣，刘青小心翼翼地在旁边走着的场景。从南平快速下来后，刘青和巡防员将女子带到了附近的坂田派出所。\\r\\n那姑娘到底是遭遇了什么样的事情\\r\\n才会说\\r\\n“要是你也遭遇我的事，你也会这样”\\r\\n据警方透露，该女子姓陈，系湖北人，今年44岁，据家属反映其有精神病史。三天前，陈某从老家来深圳约会网友，但约会受挫导致情绪异常，女子遂产生轻生念头。\\r\\n目前\\r\\n陈某已经被送往深圳某精神病医院进行治疗\\r\\n大大君只希望姑娘能早点康复\\r\\n其实真爱的到来并不存在年龄的限制\\r\\n你们说呢？\\r\\n因善良的原因\\r\\n一众网友纷纷为\\r\\n交警暖男点ZAN\\r\\n@弓常yan桦：就想问这个小哥哥有女票吗\\r\\n@原谅我这一辈子浪荡不羁爱萨摩耶：有什么过不去的要轻生嘛？ 想想自己的家人。同时也感谢交警蜀黍\\r\\n@火心聆听心灵：点赞交警\\r\\n@中華云盾：警察……警察就是群众最需时申出援手\\r\\n@Tomchlee：蜀黍帅！\\r\\n@SJ-李赫海i：这个交警很暖有木有！\\r\\n男子迷奸网友拍418个视频 女方从20岁到50岁不等\\r\\n去年6月7号上午，淮安市涟水县公安局刑警大队突然接到了一个奇怪的报警电话，一名女子言语不清，声称自己遭到了侵害。女子、被侵害、言语不清，几个关键词令接到电话的民警瞬间紧张起来。\\r\\n'"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_content_continous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_sentence(sentence):\n",
    "    pattern = re.compile('[。，,.]：')\n",
    "    split = pattern.sub(' ', sentence).split()  # split sentence\n",
    "    return split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2013 3 月份官方突然宣布回归中国，预示着很快就有夏普新机在中国登场了。那么，第一款夏普手机什么时候登陆中国呢？又会是怎么样的手机呢？ 近日，一款型号为 FS8016 的夏普神秘新机悄然出现在 GeekBench 的跑分库上。从其中相关信息了解到，这款机子并非旗舰定位，所搭载的是高通骁龙 660 660 是高通今年最受瞩目的芯片之一，采用 14 纳米工艺，八个 Kryo 260 核心设计，集成 Adreno 512 GPU 和 X12 LTE 660 OPPO 660 OPPO 660 2013 年推出全球首款全面屏手机 EDGEST 302SH 至今，夏普手机推出了多达 28 款的全面屏手机。 在 5'"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "' '.join(get_summarization_simple_with_text_rank(sharp_news, constraint=250))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_summarization_simple_with_text_rank(text, constraint=200):\n",
    "    return get_summarization_simple(text, sentence_ranking_by_text_ranking, constraint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentence_ranking_by_text_ranking(split_sentence):\n",
    "    sentence_graph = get_connect_graph_by_text_rank(' '.join(split_sentence))\n",
    "    ranking_sentence = networkx.pagerank(sentence_graph)\n",
    "    ranking_sentence = sorted(ranking_sentence.items(), key=lambda x: x[1], reverse=True)\n",
    "    return ranking_sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_summarization_simple(text, score_fn, constraint=200):\n",
    "    sub_sentence = split_sentence(text)\n",
    "    ranking_sentence = score_fn(sub_sentence)\n",
    "    selected_text = set()\n",
    "    current_text = ''\n",
    "    \n",
    "    for sen, _ in ranking_sentence:\n",
    "        if len(current_text) < constraint:\n",
    "            current_text += sen\n",
    "            selected_text.add(sen)\n",
    "        else:\n",
    "            break\n",
    "\n",
    "    summarized = []\n",
    "    for sen in sub_sentence:  # print the selected sentence by sequent\n",
    "        if sen in selected_text:\n",
    "            summarized.append(sen)\n",
    "    return summarized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 课间休息 15min \n",
    "## 10：37 课程继续"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'虽然至今夏普智能手机在市场上无法排得上号，已经完全没落，并于 2013 年退出中国市场，但是今年 3 月份官方突然宣布回归中国，预示着很快就有夏普新机在中国登场了。那么，第一款夏普手机什么时候登陆中国呢？又会是怎么样的手机呢？\\r\\n近日，一款型号为 FS8016 的夏普神秘新机悄然出现在 GeekBench 的跑分库上。从其中相关信息了解到，这款机子并非旗舰定位，所搭载的是高通骁龙 660 处理器，配备有 4GB 的内存。骁龙 660 是高通今年最受瞩目的芯片之一，采用 14 纳米工艺，八个 Kryo 260 核心设计，集成 Adreno 512 GPU 和 X12 LTE 调制解调器。\\r\\n当前市面上只有一款机子采用了骁龙 660 处理器，那就是已经上市销售的 OPPO R11。骁龙 660 尽管并非旗舰芯片，但在多核新能上比去年骁龙 820 强，单核改进也很明显，所以放在今年仍可以让很多手机变成高端机。不过，由于 OPPO 与高通签署了排他性协议，可以独占两三个月时间。\\r\\n考虑到夏普既然开始测试新机了，说明只要等独占时期一过，夏普就能发布骁龙 660 新品了。按照之前被曝光的渲染图了解，夏普的新机核心竞争优势还是全面屏，因为从 2013 年推出全球首款全面屏手机 EDGEST 302SH 至今，夏普手机推出了多达 28 款的全面屏手机。\\r\\n在 5 月份的媒体沟通会上，惠普罗忠生表示：“我敢打赌，12 个月之后，在座的各位手机都会换掉。因为全面屏时代的到来，我们怀揣的手机都将成为传统手机。”\\r\\n'"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pure_content.iloc[6]['content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2013 年退出中国市场 但是今年 3 月份官方突然宣布回归中国 预示着很快就有夏普新机在中国登场了 那么 第一款夏普手机什么时候登陆中国呢？又会是怎么样的手机呢？ 近日 一款型号为 FS8016 660 处理器 骁龙 660 260 核心设计 集成 Adreno 512 660 处理器 OPPO 骁龙 660 OPPO 660 2013 EDGEST 302SH 至今 夏普手机推出了多达 28 款的全面屏手机 在 5 月份的媒体沟通会上 惠普罗忠生表示：“我敢打赌 12 个月之后 在座的各位手机都会换掉 因为全面屏时代的到来'"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "' '.join(get_summarization_simple(pure_content.iloc[6]['content']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'原标题：中国墨子号卫星首次实现1200公里量子纠缠，震惊国外专家\\r\\n雷锋网按：在量子加密通信的研究领域，如何长距离传输纠缠光子一直是个很大的难题。不过最近我国的科学家们，利用去年八月发射的墨子号量子卫星，在这件事上取得了一些突破。研究者们成功从太空中，往相距约 1200 公里的云南丽江和青海德令哈地面站发送了纠缠光子对。本文由雷锋网编译。\\r\\n去年年底的某个晴朗的夜晚，一个绿色的星点出现在中缅边界的地平线附近。正在丽江郊外观测站的物理学家，中国科技大学教授陆朝阳观测到了这一现象，他说：“这很像一个非常明亮的绿色星体。”\\r\\n陆教授和他的同事们必须赶快采取行动。因为那个绿色星体其实是来自于 300 英里上空正在运行的卫星发出的一道激光，它就像一个灯塔预示着太空飞行器的位置。激光点在空中快速移动着，10 分钟后就会消失在地平线中。所以这个由中国的多个科学机构研究者组成的团队，正在用望远镜紧紧盯着这道绿光，努力捕捉着其中最重要的东西：这个卫星上由特殊晶体制造的一种微妙的单一红外光子。最终他们过滤掉绿光，锁定了他们的“猎物”——一个过去从未发出射过的量子信号。\\r\\n这项实验是量子密码这种新技术中的一次关键测试。量子密码就是像光子这样的量子传输安全信息的技术。但是众所周知，脆弱的量子不易进行传递，如果你试图利用光纤来传递它们，超过 150 英里后，信号就会失效，这种性质使得量子密码在全国或者世界范围内传递消息时起不到什么作用。\\r\\n所以科学家们一直在研究如何利用卫星来进行长距离的量子传送。但是在此之前，还没有人做到如此远的距离。在这项实验中，中国科学家在两个相距 750 英里的地面站点和卫星之间传递单一光子，创造了距离最远的新记录（两站分别是青海德令哈站和云南丽江高美古站，两站距离1203公里）。参与这项工作的巴黎量子计算中心副主管 Eleni Diamanti 说：“这个实验对扩展远距离量子沟通网络来讲拥有里程碑式的意义，这毫无疑问。”\\r\\n去年 8 月，在戈壁滩的酒泉卫星发射中心，中国发射了造价一亿美元的量子卫星“墨子号”，专门用来进行空间级别的量子实验。该卫星上搭载了一套复杂的激光系统、反射镜面系统和一中特殊的晶体，当激光反射在晶体上时，它会创造出一对处于纠缠态的光子。晶体一次可以制造 6 百万对光子，但是地面上的两个站点每秒只能探测到大约一对光子。陆教授说：“这项任务非常具有挑战性，类似于你在 300 米外观察一根头发。”\\r\\n陆教授和他的同事们认为，量子密码技术会成为未来一种良好的加密工具，其工作原理是这样的：首先，测量光子的性质，得到一串由 0、1 组成的密钥，接着利用这串密钥加密你的信息并将其发送给特定的接收者。如果黑客想要在传输中窃取这串密钥，根据测不准理论，量子将会在窃取的瞬间改变密钥数字。想象薛定谔的猫，当你没有观察它时，它既生又死，而你一旦观察它，它就会表现出生或者死的一个状态。同样的，偷窃的黑客会瞬间改变构成密钥的光子的状态，所以理论上，在理想状态下，这串密钥绝对不会被窃取（现实中的硬件设备并不完美，探测器在探测连续单一光子时表现不佳，这可能让我们误以为信息被窃取了，黑客也可以通过发射强光来追踪你的探测器）。\\r\\n中国的量子卫星发射和这项实验是科研人员长期努力的结果。负责这个项目的物理学家，中国科技大学教授潘建伟说，卫星实验的开始可以追溯到2003年，他带领的大约 100 人的团队从设计、建造到调整激光和卫星系统付出了多年的努力。他们最初的实验是在地面上进行的，起初只是在几英里内传输密钥，后来慢慢开始加大距离。\\r\\n“但是他们在该领域的研究仍然是很快的。”加拿大滑铁卢大学的物理学 Thomas Jennewein 说道（他最近完成了从地面到飞行中的飞机间的量子传输）。几年前，Jennewein 在国际空间站上试图完成相似的实验。他说：“因为各种实验的复杂性、高昂的成本等等，那些项目没有一个可以实现如此远的距离。但是中国团队现在做到了，他们走在了领域的前沿，这非常棒。”\\r\\n杜克大学的中国科技政策研究专家 Denis Simon 说：“他们之所以行动如此快速，得益于中国政府对该项目的充分重视。因为中国政府领导希望完成这样的实验，所以实验团队无需在通常的官僚制度上浪费时间。”中国政府对量子通信技术抱有极大的兴趣，因为量子安全通信对国家利益大有裨益。他说道：“中国政府想将这种通信技术运用在中国南海的海军战舰上，该技术的应用还有很多。”\\r\\n同时，其他国家的科学家也在进行类似的实验，但是却被很多官僚制度所束缚。比如 Diamanti 的团队还正在等待欧洲空间局对他们在国际空间站和欧洲几个地面站点间传递量子实验申请的回复。伊利诺伊香槟分校的物理学家 Paul Kwiat 也正在领导美国的团队与 NASA 合作进行相似的实验。\\r\\n但是还没有一个国家像中国一样对量子通信有着宏伟的计划。陆教授和他的团队正在计划在一个新的更远的卫星上实施同样的实验，将量子通信的距离从城市间扩展得到更远。他们想要在中国和奥地利（那里有一些合作伙伴）之间交换量子密钥。潘建伟曾说过，中国计划在 2030 年打造一个覆盖全球的量子卫星通信网络。陆教授说：“我们是非常幸运的，我们的成功得益于中国政府的快速决策系统，政治和科学的结合可以事半功倍。”\\r\\n'"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pure_content.iloc[2496]['content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2496----\n",
      "如何长距离传输纠缠光子一直是个很大的难题 不过最近我国的科学家们 利用去年八月发射的墨子号量子卫星 在这件事上取得了一些突破 研究者们成功从太空中 往相距约 1200 ” 300 Diamanti ” 300 ” 在理想状态下 这串密钥绝对不会被窃取（现实中的硬件设备并不完美 探测器在探测连续单一光子时表现不佳 这可能让我们误以为信息被窃取了 黑客也可以通过发射强光来追踪你的探测器） 中国的量子卫星发射和这项实验是科研人员长期努力的结果 Jennewein Jennewein ” ” Diamanti 年打造一个覆盖全球的量子卫星通信网络 ”\n"
     ]
    }
   ],
   "source": [
    "for index in random.sample(list(range(len(pure_content))), 30):\n",
    "    content = pure_content.iloc[index]['content']\n",
    "    if len(content) > 1000:\n",
    "        print('{}----'.format(index))\n",
    "        print(' '.join(get_summarization_simple(content)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('一名女子言语不清', 0.008761809701694283),\n",
       " ('行走在南坪快速上', 0.008761809701694281),\n",
       " ('期间还起了轻生年头', 0.00853806788025346),\n",
       " ('淮安市涟水县公安局刑警大队突然接到了一个奇怪的报警电话', 0.00853806788025346),\n",
       " ('一辅警发现后赶紧为其披上黄衣', 0.008371719895408159),\n",
       " ('去年6月7号上午', 0.008371719895408159),\n",
       " ('并一路劝说她', 0.008245759268329758),\n",
       " ('女方从20岁到50岁不等', 0.008245759268329758),\n",
       " ('那么事发时', 0.00815029910456809),\n",
       " ('男子迷奸网友拍418个视频', 0.00815029910456809),\n",
       " ('@SJ-李赫海i：这个交警很暖有木有！', 0.008078401421739694),\n",
       " ('到底都发生了些什么呢？', 0.008078401421739692),\n",
       " ('南都记者带您一起还原现场', 0.008024087516959199),\n",
       " ('@Tomchlee：蜀黍帅！', 0.008024087516959199),\n",
       " ('南都记者在龙岗大队坂田中队见到了辅警刘青（发现女生的辅警）', 0.007983207314774927),\n",
       " ('@中華云盾：警察……警察就是群众最需时申出援手', 0.007983207314774927),\n",
       " ('一位外表高大帅气', 0.007952624377193676),\n",
       " ('@火心聆听心灵：点赞交警', 0.007952624377193674),\n",
       " ('说话略带些腼腆的90后青年', 0.007929884597094566),\n",
       " ('同时也感谢交警蜀黍', 0.007929884597094566),\n",
       " ('刘青介绍', 0.007913142240506744),\n",
       " ('想想自己的家人', 0.007913142240506744),\n",
       " ('@原谅我这一辈子浪荡不羁爱萨摩耶：有什么过不去的要轻生嘛？', 0.007900964870248895),\n",
       " ('6月16日早上7时36分', 0.007900964870248892),\n",
       " ('他正在环城南路附近值勤', 0.007892232774304273),\n",
       " ('@弓常yan桦：就想问这个小哥哥有女票吗', 0.007892232774304273),\n",
       " ('接到中队关于一位女子裸身进入机动车可能有危险的警情', 0.00788607704919873),\n",
       " ('交警暖男点ZAN', 0.00788607704919873),\n",
       " ('一众网友纷纷为', 0.007881820981426759),\n",
       " ('随后骑着小铁骑开始沿路寻找', 0.007881820981426757),\n",
       " ('因善良的原因', 0.007878941618434662),\n",
       " ('大概花了十多分钟在南坪大道坂田出口往龙岗方向的逆行辅道上发现该女子', 0.00787894161843466),\n",
       " ('你们说呢？', 0.007877040414333012),\n",
       " ('女子身上一丝不挂地逆车流而行', 0.00787704041433301),\n",
       " ('时走时停', 0.007875818243366639),\n",
       " ('其实真爱的到来并不存在年龄的限制', 0.007875818243366639),\n",
       " ('时坐时躺', 0.007875055326830038),\n",
       " ('大大君只希望姑娘能早点康复', 0.007875055326830038),\n",
       " ('险象环生', 0.007874594202822473),\n",
       " ('陈某已经被送往深圳某精神病医院进行治疗', 0.007874594202822473),\n",
       " ('目前', 0.007874325165885149),\n",
       " ('刘青停好小铁骑', 0.007874325165885147),\n",
       " ('女子遂产生轻生念头', 0.00787417418096783),\n",
       " ('和另外一名巡防员追了上去', 0.007874174180967829),\n",
       " ('但约会受挫导致情绪异常', 0.007874093011541456),\n",
       " ('发现女子的情绪很低落', 0.007874093011541454),\n",
       " ('话不多', 0.007874051412730365),\n",
       " ('陈某从老家来深圳约会网友', 0.007874051412730365),\n",
       " ('刘青尝试和女子交流', 0.007874031209340058),\n",
       " ('三天前', 0.007874031209340058),\n",
       " ('劝说女子离开', 0.007874021979828482),\n",
       " ('据家属反映其有精神病史', 0.007874021979828482),\n",
       " ('今年44岁', 0.00787401805200212),\n",
       " ('可女子并不愿意接受', 0.007874018052002118),\n",
       " ('继续缓慢地往南坪快速路的主干道上走去', 0.007874016514985274),\n",
       " ('系湖北人', 0.007874016514985274),\n",
       " ('此时路边上已经聚集了很市民围观', 0.007874015971942193),\n",
       " ('该女子姓陈', 0.007874015971942193),\n",
       " ('为了不刺激女子的情绪', 0.007874015803169621),\n",
       " ('据警方透露', 0.007874015803169621),\n",
       " ('你也会这样”', 0.007874015758793634),\n",
       " ('刘青和巡防员一边盯着女子一边驱赶着围观的群众', 0.007874015758793632),\n",
       " ('现场还原', 0.007874015749519759),\n",
       " ('“要是你也遭遇我的事', 0.007874015749519759),\n",
       " ('从警方提供的一份视频了解到', 0.00787401574814256),\n",
       " ('才会说', 0.00787401574814256),\n",
       " ('16日早上7时25分', 0.007874015748031496),\n",
       " ('女子出现在坂雪岗大道与环城南路的监控视频中', 0.007874015748031496),\n",
       " ('此时女子还穿着白色的内裤', 0.007874015748031496),\n",
       " ('正沿着坂雪岗大道往南坪快速的方向缓慢地走着', 0.007874015748031496),\n",
       " ('当时正值上班高峰期', 0.007874015748031496),\n",
       " ('十字路口的车流已经排起了长队', 0.007874015748031496),\n",
       " ('当女子出现时', 0.007874015748031496),\n",
       " ('路上的市民纷纷驻足观望', 0.007874015748031496),\n",
       " ('不少车辆也放慢了速度', 0.007874015748031496),\n",
       " ('但女子并不为市民观望停下脚步', 0.007874015748031496),\n",
       " ('依然缓慢走着', 0.007874015748031496),\n",
       " ('当女子行进到十字路口中间时', 0.007874015748031496),\n",
       " ('一辆大货车挡住了镜头', 0.007874015748031496),\n",
       " ('但是当女子再次出现镜头时', 0.007874015748031496),\n",
       " ('可以发现女子已经没穿内裤了', 0.007874015748031496),\n",
       " ('全身裸露继续朝着南坪快速方向走去', 0.007874015748031496),\n",
       " ('记者发现', 0.007874015748031496),\n",
       " ('视频中女子周围并没有人尾随或者上前劝止的市民', 0.007874015748031496),\n",
       " ('一大清早路上看到这样的情况', 0.007874015748031496),\n",
       " ('恐怕大家都没办法淡定', 0.007874015748031496),\n",
       " ('面对这一情况', 0.007874015748031496),\n",
       " ('刘青表示', 0.007874015748031496),\n",
       " ('“一开始根本不敢看她', 0.007874015748031496),\n",
       " ('心里挺别扭', 0.007874015748031496),\n",
       " ('感觉很尴尬”', 0.007874015748031496),\n",
       " ('但当刘青跟随女子上了南坪快速路主干道时', 0.007874015748031496),\n",
       " ('女子作出了让人意想不到的举动', 0.007874015748031496),\n",
       " ('她突然靠近护栏要从上面跳下去', 0.007874015748031496),\n",
       " ('刘青赶忙冲上去拉住了女子的手', 0.007874015748031496),\n",
       " ('将其控制住并远离护栏', 0.007874015748031496),\n",
       " ('碍于女子没有穿衣服', 0.007874015748031496),\n",
       " ('刘青递上衣服', 0.007874015748031496),\n",
       " ('女子没接受还把衣服扔到排水沟里', 0.007874015748031496),\n",
       " ('继续往前走', 0.007874015748031496),\n",
       " ('没办法刘青只能紧紧拉着她的一只手跟在后面', 0.007874015748031496),\n",
       " ('刘青一路上耐心地开导安慰她', 0.007874015748031496),\n",
       " ('但只听到她不断地重复着一句话“要是你也遭遇我的事', 0.007874015748031496),\n",
       " ('你也会这样的”', 0.007874015748031496),\n",
       " ('期间她还不时试图挣脱刘青的手要冲向护栏往下跳', 0.007874015748031496),\n",
       " ('就这样', 0.007874015748031496),\n",
       " ('我被牵着走了大概十多分钟', 0.007874015748031496),\n",
       " ('天突然下起了大暴雨', 0.007874015748031496),\n",
       " ('雨大的连眼睛都睁不开”刘青继续说着', 0.007874015748031496),\n",
       " ('瞬间他们就被雨透了', 0.007874015748031496),\n",
       " ('但女子依然不愿意接受刘青的帮助', 0.007874015748031496),\n",
       " ('就继续冒着大雨往前走', 0.007874015748031496),\n",
       " ('大概走了有四十分钟吧', 0.007874015748031496),\n",
       " ('女子突然停下来说“我想回家了”', 0.007874015748031496),\n",
       " ('然后女子也接受了刘青递过来的小黄衣', 0.007874015748031496),\n",
       " ('就出现了深圳微博上的照片', 0.007874015748031496),\n",
       " ('女子披着小黄衣', 0.007874015748031496),\n",
       " ('刘青小心翼翼地在旁边走着的场景', 0.007874015748031496),\n",
       " ('从南平快速下来后', 0.007874015748031496),\n",
       " ('刘青和巡防员将女子带到了附近的坂田派出所', 0.007874015748031496),\n",
       " ('那姑娘到底是遭遇了什么样的事情', 0.007874015748031496),\n",
       " ('声称自己遭到了侵害', 0.007760391420088102),\n",
       " ('@深圳交警微博称：昨日清晨交警发现有一女子赤裸上身', 0.007760391420088101),\n",
       " ('暴雨中裸身奔走……）', 0.006743520732558356),\n",
       " ('女子、被侵害、言语不清', 0.006743520732558355),\n",
       " ('（原标题：44岁女子跑深圳约会网友被拒', 0.005706295418150805),\n",
       " ('几个关键词令接到电话的民警瞬间紧张起来', 0.005706295418150804)]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(ranking_sentence.items(), key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'decode'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-183-a20865cbc625>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mkeywords_graph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_connect_graph_by_text_rank\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfirst_content\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwindow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-176-b285888f805a>\u001b[0m in \u001b[0;36mget_connect_graph_by_text_rank\u001b[0;34m(tokenized_text, window)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mii\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokeners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         word_tuples = [(tokeners[connect].decode('utf-8'), t.decode('utf-8')) \n\u001b[0;32m----> 6\u001b[0;31m                        \u001b[0;32mfor\u001b[0m \u001b[0mconnect\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mii\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mii\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m                        if connect >= 0 and connect < len(tokeners)]\n\u001b[1;32m      8\u001b[0m         \u001b[0mkeywords_graph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_edges_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_tuples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-176-b285888f805a>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      5\u001b[0m         word_tuples = [(tokeners[connect].decode('utf-8'), t.decode('utf-8')) \n\u001b[1;32m      6\u001b[0m                        \u001b[0;32mfor\u001b[0m \u001b[0mconnect\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mii\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mii\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m                        if connect >= 0 and connect < len(tokeners)]\n\u001b[0m\u001b[1;32m      8\u001b[0m         \u001b[0mkeywords_graph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_edges_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_tuples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'decode'"
     ]
    }
   ],
   "source": [
    "keywords_graph = get_connect_graph_by_text_rank(first_content, window=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranking_words = networkx.pagerank(keywords_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import rcParams\n",
    "rcParams['font.family'] = '/Users/mqgao/Downloads/SourceHanSerifSC-Regular.otf'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/ibm/lib/python3.6/site-packages/matplotlib/font_manager.py:1328: UserWarning: findfont: Font family ['/Users/mqgao/Downloads/SourceHanSerifSC-Regular.otf'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "networkx.draw_networkx(keywords_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('，', 0.04971796909107254),\n",
       " ('的', 0.032169208603727614),\n",
       " ('。', 0.02860228225909264),\n",
       " ('、', 0.02771502328849811),\n",
       " ('骁龙', 0.017759850248184172),\n",
       " ('Windows', 0.015115542829056361),\n",
       " ('10', 0.014661459543306039),\n",
       " ('835', 0.01417195081727616),\n",
       " ('桌面', 0.013938442660657342),\n",
       " ('平台', 0.012843338290542764)]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(ranking_words.items(), key=lambda x: x[1], reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentence Embedding Based"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06041265049289309"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(frequence.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'测试机' in model.wv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/ibm/lib/python3.6/site-packages/ipykernel_launcher.py:14: DeprecationWarning: Call to deprecated `__contains__` (Method will be removed in 4.0.0, use self.wv.__contains__() instead).\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.8209102 ,  0.06840289, -0.53223395, -0.735713  , -0.24113654,\n",
       "       -0.95707494,  0.9833474 , -0.74113065,  0.31412685, -2.0497446 ,\n",
       "        2.0620635 , -1.0265256 ,  1.092847  , -0.853559  , -1.8556648 ,\n",
       "       -0.93276614,  0.2515715 , -1.020293  , -0.05743051, -1.8814358 ,\n",
       "        0.5998357 , -0.06820183,  2.357323  ,  0.83137274, -1.2140547 ,\n",
       "       -3.565163  ,  0.9846055 , -0.77957344, -0.2384383 ,  1.178894  ,\n",
       "       -0.96436185,  3.207032  ,  0.897161  , -0.5350476 ,  0.40027657],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_embedding('这是 一台 测试机')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentence_embedding(sentence, cut):\n",
    "    # weight = alpah/(alpah + p) \n",
    "    # alpha is a parameter, 1e-3 ~ 1e-5\n",
    "    alpha = 1e-4\n",
    "    \n",
    "    global frequence\n",
    "    \n",
    "    max_fre = max(frequence.values())\n",
    "    \n",
    "    words = cut(sentence).split()\n",
    "    \n",
    "    sentence_vec = np.zeros_like(model.wv['测试'])\n",
    "    \n",
    "    words = [w for w in words if w in model]\n",
    "    \n",
    "    for w in words:\n",
    "        weight = alpha / (alpha + frequence.get(w, max_fre))\n",
    "        sentence_vec += weight * model.wv[w]\n",
    "    \n",
    "    sentence_vec /= len(words)\n",
    "    # Skip the PCA\n",
    "    return sentence_vec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Treat the whole text as a long sentence.\n",
    "\n",
    "```\n",
    "\n",
    "# 程序伪代码\n",
    "sentence_vector = get_sentence_embedding(text) \n",
    "# sentence vector is represeted as semantic similarity\n",
    "# therefore, sentence vector is representing the whole text semantic infomation.\n",
    "\n",
    "correlations = []\n",
    "\n",
    "for sub_sentence in text:\n",
    "    sub_sen_vec = get_sentence_embedding(sub_sentence)\n",
    "    correlation = similarity(sub_sen_vec, sentence_vector)\n",
    "    correlations.append(correlation)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "sharp_news = pure_content.iloc[6]['content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import cosine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_corrlations(text, cut_fn):\n",
    "    if isinstance(text, list): text = ' '.join(text)\n",
    "        \n",
    "    sub_sentences = split_sentence(text)\n",
    "    sentence_vector = sentence_embedding(text, cut_fn)\n",
    "        \n",
    "    correlations = {}\n",
    "    \n",
    "    for sub_sentence in sub_sentences:\n",
    "        sub_sen_vec = sentence_embedding(sub_sentence, cut_fn)\n",
    "        correlation = cosine(sentence_vector, sub_sen_vec)\n",
    "        correlations[sub_sentence]= correlation\n",
    "\n",
    "    return sorted(corrlations.items(), key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "# f(x, y) \n",
    "#f(x, y=1) # is a partial funtion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrlations_with_global_cut = partial(get_corrlations, cut_fn=cut)\n",
    "corrlations_with_global_cut = partial(get_corrlations, cut_fn=another_cut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_summarization_algorithm(): pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function get_summarization_simple in module __main__:\n",
      "\n",
      "get_summarization_simple(text, score_fn, constraint=200)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(get_summarization_simple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_summarization_simple_by_sen_embedding(text, constraint=200):\n",
    "    return get_summarization_simple(text, corrlations_with_global_cut, constraint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/ibm/lib/python3.6/site-packages/ipykernel_launcher.py:14: DeprecationWarning: Call to deprecated `__contains__` (Method will be removed in 4.0.0, use self.wv.__contains__() instead).\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'2013 年退出中国市场，但是今年 3 近日，一款型号为 FS8016 的夏普神秘新机悄然出现在 GeekBench 660 处理器，配备有 4GB 660 14 纳米工艺，八个 Kryo 260 核心设计，集成 Adreno 512 GPU 和 X12 LTE 调制解调器。 660 OPPO R11。骁龙 660 820 OPPO 与高通签署了排他性协议，可以独占两三个月时间。 660 2013 年推出全球首款全面屏手机 EDGEST 302SH 28 在 5 月份的媒体沟通会上，惠普罗忠生表示：“我敢打赌，12'"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "' '.join(get_summarization_simple_by_sen_embedding(sharp_news, constraint=200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "danger_news = pure_content.iloc[4]['content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sequent_correlation(text):\n",
    "    sub_sentence = split_sentence(text)\n",
    "    corrleations = corrlations_with_global_cut(sub_sentence)\n",
    "    correlation_with_sequent = [(sen, corrlations[sen]) for sen in sub_sentence]\n",
    "    return correlation_with_sequent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/ibm/lib/python3.6/site-packages/ipykernel_launcher.py:14: DeprecationWarning: Call to deprecated `__contains__` (Method will be removed in 4.0.0, use self.wv.__contains__() instead).\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('虽然至今夏普智能手机在市场上无法排得上号，已经完全没落，并于', 0.12964648008346558),\n",
       " ('2013', 0.799274817109108),\n",
       " ('年退出中国市场，但是今年', 0.30392885208129883),\n",
       " ('3', 1.0643607676029205),\n",
       " ('月份官方突然宣布回归中国，预示着很快就有夏普新机在中国登场了。那么，第一款夏普手机什么时候登陆中国呢？又会是怎么样的手机呢？',\n",
       "  0.12079513072967529),\n",
       " ('近日，一款型号为', 0.24997162818908691),\n",
       " ('FS8016', 1.0181109383702278),\n",
       " ('的夏普神秘新机悄然出现在', 0.19331711530685425),\n",
       " ('GeekBench', 1.0206618532538414),\n",
       " ('的跑分库上。从其中相关信息了解到，这款机子并非旗舰定位，所搭载的是高通骁龙', 0.04037266969680786),\n",
       " ('660', 0.8802495077252388),\n",
       " ('处理器，配备有', 0.2487751841545105),\n",
       " ('4GB', 0.8022160232067108),\n",
       " ('的内存。骁龙', 0.1610603928565979),\n",
       " ('660', 0.8802495077252388),\n",
       " ('是高通今年最受瞩目的芯片之一，采用', 0.1596813201904297),\n",
       " ('14', 1.0912874341011047),\n",
       " ('纳米工艺，八个', 0.20669978857040405),\n",
       " ('Kryo', 1.261707752943039),\n",
       " ('260', 0.9195282161235809),\n",
       " ('核心设计，集成', 0.2239118218421936),\n",
       " ('Adreno', 1.0604844614863396),\n",
       " ('512', 0.9973529151175171),\n",
       " ('GPU', 0.6360308527946472),\n",
       " ('和', 0.5902390778064728),\n",
       " ('X12', 1.0669185444712639),\n",
       " ('LTE', 0.6847786903381348),\n",
       " ('调制解调器。', 0.4060221314430237),\n",
       " ('当前市面上只有一款机子采用了骁龙', 0.17805546522140503),\n",
       " ('660', 0.8802495077252388),\n",
       " ('处理器，那就是已经上市销售的', 0.055647075176239014),\n",
       " ('OPPO', 0.7231224775314331),\n",
       " ('R11。骁龙', 0.38088226318359375),\n",
       " ('660', 0.8802495077252388),\n",
       " ('尽管并非旗舰芯片，但在多核新能上比去年骁龙', 0.16877931356430054),\n",
       " ('820', 0.9291320592164993),\n",
       " ('强，单核改进也很明显，所以放在今年仍可以让很多手机变成高端机。不过，由于', 0.09469187259674072),\n",
       " ('OPPO', 0.7231224775314331),\n",
       " ('与高通签署了排他性协议，可以独占两三个月时间。', 0.2567896246910095),\n",
       " ('考虑到夏普既然开始测试新机了，说明只要等独占时期一过，夏普就能发布骁龙', 0.07963865995407104),\n",
       " ('660', 0.8802495077252388),\n",
       " ('新品了。按照之前被曝光的渲染图了解，夏普的新机核心竞争优势还是全面屏，因为从', 0.040095508098602295),\n",
       " ('2013', 0.799274817109108),\n",
       " ('年推出全球首款全面屏手机', 0.5124390721321106),\n",
       " ('EDGEST', 1.1215405240654945),\n",
       " ('302SH', 0.9462662227451801),\n",
       " ('至今，夏普手机推出了多达', 0.13676100969314575),\n",
       " ('28', 1.1545579135417938),\n",
       " ('款的全面屏手机。', 0.11287486553192139),\n",
       " ('在', 0.4273228049278259),\n",
       " ('5', 1.174242302775383),\n",
       " ('月份的媒体沟通会上，惠普罗忠生表示：“我敢打赌，12', 0.1848764419555664),\n",
       " ('个月之后，在座的各位手机都会换掉。因为全面屏时代的到来，我们怀揣的手机都将成为传统手机。”', 0.045291781425476074)]"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_sentence = get_sequent_correlation(sharp_news)\n",
    "sub_sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1406251d0>]"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/ibm/lib/python3.6/site-packages/matplotlib/font_manager.py:1328: UserWarning: findfont: Font family ['/Users/mqgao/Downloads/SourceHanSerifSC-Regular.otf'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(sub_sentence)), [y for _, y in correlation_with_sequent])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/ibm/lib/python3.6/site-packages/ipykernel_launcher.py:14: DeprecationWarning: Call to deprecated `__contains__` (Method will be removed in 4.0.0, use self.wv.__contains__() instead).\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "sub_seq_of_danger = get_sequent_correlation(danger_news)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_seq_of_danger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x140f9cf28>]"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/ibm/lib/python3.6/site-packages/matplotlib/font_manager.py:1328: UserWarning: findfont: Font family ['/Users/mqgao/Downloads/SourceHanSerifSC-Regular.otf'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(sub_seq_of_danger)), [y for _, y in sub_seq_of_danger])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supplementary\n",
    "\n",
    "这些对TextRank和Sentence Emebdding都是有效用的。 \n",
    "\n",
    "### 1. KNN make it more fluently\n",
    "> 对于一个sub_sentence来说，它的重要性，取决于他本身的重要性和周围的句子(neighbors)的重要性的综合；\n",
    "### 2. Title is very important is it exists\n",
    "> 如果一个文章有标题，那么，其实这个标题已经包含了很多它的摘要信息， 在考虑语义建模的时候，就应该对标题更加重要的考虑；\n",
    "### 3. The keywords count; \n",
    "> 关键字是很重要的，如果我们能够比较准确的提取出来关键词/字，然后对关键字/词包含的句子增加其权重； \n",
    "### 4. Position counts;\n",
    "> 对于一些文本，文章，例如一个故事的这种文章，那么他的textRank， sentence embedding 会发现，并不会出现很明显有些句子是重要的，有些句子不重要的，如果plot他的曲线的话，我们这个时候就要考虑他的位置，开头，结尾，增加一些权重；\n",
    "### 5. Topics count; \n",
    "> LDA主题模型，得到的主题分布，我们可以用每句话和的出来的这些主题进行相似度对比，我们不仅仅是是考虑他的整个的text， 我们还有考虑主题；\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/ibm/lib/python3.6/site-packages/ipykernel_launcher.py:14: DeprecationWarning: Call to deprecated `__contains__` (Method will be removed in 4.0.0, use self.wv.__contains__() instead).\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "corrlations = get_corrlations(sharp_news, cut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_summarization_simple()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Kryo', 1.261707752943039),\n",
       " ('5', 1.174242302775383),\n",
       " ('28', 1.1545579135417938),\n",
       " ('EDGEST', 1.1215405240654945),\n",
       " ('14', 1.0912874341011047),\n",
       " ('X12', 1.0669185444712639),\n",
       " ('3', 1.0643607676029205),\n",
       " ('Adreno', 1.0604844614863396),\n",
       " ('GeekBench', 1.0206618532538414),\n",
       " ('FS8016', 1.0181109383702278),\n",
       " ('512', 0.9973529151175171),\n",
       " ('302SH', 0.9462662227451801),\n",
       " ('820', 0.9291320592164993),\n",
       " ('260', 0.9195282161235809),\n",
       " ('660', 0.8802495077252388),\n",
       " ('4GB', 0.8022160232067108),\n",
       " ('2013', 0.799274817109108),\n",
       " ('OPPO', 0.7231224775314331),\n",
       " ('LTE', 0.6847786903381348),\n",
       " ('GPU', 0.6360308527946472),\n",
       " ('和', 0.5902390778064728),\n",
       " ('年推出全球首款全面屏手机', 0.5124390721321106),\n",
       " ('在', 0.4273228049278259),\n",
       " ('调制解调器。', 0.4060221314430237),\n",
       " ('R11。骁龙', 0.38088226318359375),\n",
       " ('年退出中国市场，但是今年', 0.30392885208129883),\n",
       " ('与高通签署了排他性协议，可以独占两三个月时间。', 0.2567896246910095),\n",
       " ('近日，一款型号为', 0.24997162818908691),\n",
       " ('处理器，配备有', 0.2487751841545105),\n",
       " ('核心设计，集成', 0.2239118218421936),\n",
       " ('纳米工艺，八个', 0.20669978857040405),\n",
       " ('的夏普神秘新机悄然出现在', 0.19331711530685425),\n",
       " ('月份的媒体沟通会上，惠普罗忠生表示：“我敢打赌，12', 0.1848764419555664),\n",
       " ('当前市面上只有一款机子采用了骁龙', 0.17805546522140503),\n",
       " ('尽管并非旗舰芯片，但在多核新能上比去年骁龙', 0.16877931356430054),\n",
       " ('的内存。骁龙', 0.1610603928565979),\n",
       " ('是高通今年最受瞩目的芯片之一，采用', 0.1596813201904297),\n",
       " ('至今，夏普手机推出了多达', 0.13676100969314575),\n",
       " ('虽然至今夏普智能手机在市场上无法排得上号，已经完全没落，并于', 0.12964648008346558),\n",
       " ('月份官方突然宣布回归中国，预示着很快就有夏普新机在中国登场了。那么，第一款夏普手机什么时候登陆中国呢？又会是怎么样的手机呢？',\n",
       "  0.12079513072967529),\n",
       " ('款的全面屏手机。', 0.11287486553192139),\n",
       " ('强，单核改进也很明显，所以放在今年仍可以让很多手机变成高端机。不过，由于', 0.09469187259674072),\n",
       " ('考虑到夏普既然开始测试新机了，说明只要等独占时期一过，夏普就能发布骁龙', 0.07963865995407104),\n",
       " ('处理器，那就是已经上市销售的', 0.055647075176239014),\n",
       " ('个月之后，在座的各位手机都会换掉。因为全面屏时代的到来，我们怀揣的手机都将成为传统手机。”', 0.045291781425476074),\n",
       " ('的跑分库上。从其中相关信息了解到，这款机子并非旗舰定位，所搭载的是高通骁龙', 0.04037266969680786),\n",
       " ('新品了。按照之前被曝光的渲染图了解，夏普的新机核心竞争优势还是全面屏，因为从', 0.040095508098602295)]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'虽然至今夏普智能手机在市场上无法排得上号，已经完全没落，并于 2013 年退出中国市场，但是今年 3 月份官方突然宣布回归中国，预示着很快就有夏普新机在中国登场了。那么，第一款夏普手机什么时候登陆中国呢？又会是怎么样的手机呢？\\r\\n近日，一款型号为 FS8016 的夏普神秘新机悄然出现在 GeekBench 的跑分库上。从其中相关信息了解到，这款机子并非旗舰定位，所搭载的是高通骁龙 660 处理器，配备有 4GB 的内存。骁龙 660 是高通今年最受瞩目的芯片之一，采用 14 纳米工艺，八个 Kryo 260 核心设计，集成 Adreno 512 GPU 和 X12 LTE 调制解调器。\\r\\n当前市面上只有一款机子采用了骁龙 660 处理器，那就是已经上市销售的 OPPO R11。骁龙 660 尽管并非旗舰芯片，但在多核新能上比去年骁龙 820 强，单核改进也很明显，所以放在今年仍可以让很多手机变成高端机。不过，由于 OPPO 与高通签署了排他性协议，可以独占两三个月时间。\\r\\n考虑到夏普既然开始测试新机了，说明只要等独占时期一过，夏普就能发布骁龙 660 新品了。按照之前被曝光的渲染图了解，夏普的新机核心竞争优势还是全面屏，因为从 2013 年推出全球首款全面屏手机 EDGEST 302SH 至今，夏普手机推出了多达 28 款的全面屏手机。\\r\\n在 5 月份的媒体沟通会上，惠普罗忠生表示：“我敢打赌，12 个月之后，在座的各位手机都会换掉。因为全面屏时代的到来，我们怀揣的手机都将成为传统手机。”\\r\\n'"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sharp_news"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['虽然至今夏普智能手机在市场上无法排得上号，已经完全没落，并于',\n",
       " '2013',\n",
       " '年退出中国市场，但是今年',\n",
       " '3',\n",
       " '月份官方突然宣布回归中国，预示着很快就有夏普新机在中国登场了。那么，第一款夏普手机什么时候登陆中国呢？又会是怎么样的手机呢？',\n",
       " '近日，一款型号为',\n",
       " 'FS8016',\n",
       " '的夏普神秘新机悄然出现在',\n",
       " 'GeekBench',\n",
       " '的跑分库上。从其中相关信息了解到，这款机子并非旗舰定位，所搭载的是高通骁龙',\n",
       " '660',\n",
       " '处理器，配备有',\n",
       " '4GB',\n",
       " '的内存。骁龙',\n",
       " '660',\n",
       " '是高通今年最受瞩目的芯片之一，采用',\n",
       " '14',\n",
       " '纳米工艺，八个',\n",
       " 'Kryo',\n",
       " '260',\n",
       " '核心设计，集成',\n",
       " 'Adreno',\n",
       " '512',\n",
       " 'GPU',\n",
       " '和',\n",
       " 'X12',\n",
       " 'LTE',\n",
       " '调制解调器。',\n",
       " '当前市面上只有一款机子采用了骁龙',\n",
       " '660',\n",
       " '处理器，那就是已经上市销售的',\n",
       " 'OPPO',\n",
       " 'R11。骁龙',\n",
       " '660',\n",
       " '尽管并非旗舰芯片，但在多核新能上比去年骁龙',\n",
       " '820',\n",
       " '强，单核改进也很明显，所以放在今年仍可以让很多手机变成高端机。不过，由于',\n",
       " 'OPPO',\n",
       " '与高通签署了排他性协议，可以独占两三个月时间。',\n",
       " '考虑到夏普既然开始测试新机了，说明只要等独占时期一过，夏普就能发布骁龙',\n",
       " '660',\n",
       " '新品了。按照之前被曝光的渲染图了解，夏普的新机核心竞争优势还是全面屏，因为从',\n",
       " '2013',\n",
       " '年推出全球首款全面屏手机',\n",
       " 'EDGEST',\n",
       " '302SH',\n",
       " '至今，夏普手机推出了多达',\n",
       " '28',\n",
       " '款的全面屏手机。',\n",
       " '在',\n",
       " '5',\n",
       " '月份的媒体沟通会上，惠普罗忠生表示：“我敢打赌，12',\n",
       " '个月之后，在座的各位手机都会换掉。因为全面屏时代的到来，我们怀揣的手机都将成为传统手机。”']"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}