11.裁判文书 - 第十一步 - 地名查找.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 第一模块(导入包模块：必须运行）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 引入所有包,如果缺少某个包，包的名字附于之后\n",
    "import numpy as np\n",
    "# numpy\n",
    "\n",
    "import pandas as pd\n",
    "# pandas\n",
    "\n",
    "import requests\n",
    "# requests\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "# BeautifulSoup\n",
    "\n",
    "import re\n",
    "# re\n",
    "\n",
    "from fake_useragent import UserAgent\n",
    "# fake-useragent\n",
    "\n",
    "import json\n",
    "# json\n",
    "\n",
    "import time\n",
    "# time\n",
    "\n",
    "import random\n",
    "# random\n",
    "\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.common.keys import Keys\n",
    "# selenium\n",
    "\n",
    "from PIL import Image,ImageEnhance\n",
    "# PIL\n",
    "\n",
    "import hashlib\n",
    "# hashlib\n",
    "\n",
    "from collections import Counter\n",
    "# collections\n",
    "\n",
    "from gensim import corpora, models, similarities\n",
    "# gensim \n",
    "\n",
    "import codecs, sys\n",
    "# codecs\n",
    "\n",
    "import os\n",
    "# os\n",
    "\n",
    "import time\n",
    "#time\n",
    "\n",
    "import shutil\n",
    "# shutil\n",
    "\n",
    "import jieba\n",
    "# jieba\n",
    "\n",
    "from pyhanlp import *\n",
    "# pyhanlp，注意hanlp需要java的工具\n",
    "\n",
    "import jpype\n",
    "# jpype\n",
    "\n",
    "import tensorflow as tf\n",
    "# tensorflow\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "# matplotlib\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import feature_extraction  \n",
    "from sklearn.feature_extraction.text import TfidfTransformer  \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "# sklearn\n",
    "\n",
    "from pdfminer.converter import PDFPageAggregator\n",
    "from pdfminer.layout import LAParams\n",
    "from pdfminer.pdfparser import PDFParser, PDFDocument\n",
    "from pdfminer.pdfdevice import PDFDevice\n",
    "from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\n",
    "# pdfminer3k\n",
    "\n",
    "from tkinter import *\n",
    "from tkinter import filedialog\n",
    "# tk文件导入模块"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 第二模块(文件处理模块：必须运行）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def import_table(root):\n",
    "    \"\"\"\n",
    "    return table\"选中的表格\n",
    "    \"\"\"\n",
    "    try:\n",
    "        root.filename = filedialog.askopenfilename(filetypes=((\"xlsx\", \"*.xlsx\"),(\"xlsx\", \"*.xlsx\")))        \n",
    "        if \".xlsx\" in root.filename:\n",
    "            ### 该目录下有该文件\n",
    "            table = pd.read_excel(root.filename)\n",
    "            root.destroy()\n",
    "            return table\n",
    "    except Exception as e:\n",
    "        root.destroy()\n",
    "        print(\"导入错误\")\n",
    "\n",
    "def remote_select():\n",
    "    print(\"以下文件可以调用，需要分析哪个文件？\")\n",
    "    print(\"-------------------------------------\")\n",
    "    index = 0\n",
    "    list_item_temp = []\n",
    "    for item in os.listdir():\n",
    "        if \".xlsx\" in item:\n",
    "            index += 1\n",
    "            print(\"[\" + str(index) + \"] \" + item)\n",
    "            list_item_temp.append(item)\n",
    "    try:\n",
    "        bash_pos = \"/Users/dfuser/Desktop/目标文书目录/\"\n",
    "        file_code = int(input(\"需要导入哪个文件？(输入[]中的序号)\"))\n",
    "        final_pos = bash_pos + str(list_item_temp[file_code-1])\n",
    "        table_ = pd.read_excel(final_pos)\n",
    "        return table_\n",
    "        print(\"表格导入成功，以下是表格预览\")\n",
    "        print(\"----------------------------\")\n",
    "    except Exception as e:\n",
    "        print(\"导入错误\")\n",
    "\n",
    "def save_model(table):\n",
    "    print(\"-------------------------------------------\")\n",
    "    print(\"以下为表格的预览:\")\n",
    "    print(table.head())\n",
    "    flag = input(\"是否需要保存该词频表？(输入Y/N):\")\n",
    "    try:\n",
    "        if flag == \"Y\":\n",
    "            save_file_name = input(\"请输入该词频表格的名称(不用加.xlsx):\")\n",
    "            save_file_name_xlsx = save_file_name + \".xlsx\"\n",
    "            table.to_excel(save_file_name_xlsx)\n",
    "        else:\n",
    "            pass\n",
    "    except Exception as e:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 程序主模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你是在本机上操作？还是远程操作？(1:本机操作,2:远程操作)2\n",
      "以下文件可以调用，需要分析哪个文件？\n",
      "-------------------------------------\n",
      "[1] 夫妻债务 - 原表.xlsx\n",
      "[2] wordfreqs.xlsx\n",
      "[3] ex1.xlsx\n",
      "[4] 校园暴力 - 结构化重构.xlsx\n",
      "[5] 校园暴力事件 - 原表.xlsx\n",
      "[6] 正当防卫 - 原表.xlsx\n",
      "[7] 夫妻债务.xlsx\n",
      "[8] .~zhaiwu_regex_filter.xlsx.xlsx\n",
      "[9] 职业打假人 - 原表.xlsx\n",
      "需要导入哪个文件？(输入[]中的序号)1\n",
      "表格中所有的列: Index(['标题', '案号', '案件类型', '庭审程序', '案由', '文书类型', '法院', '判决日期', '原告', '被告',\n",
      "       '第三人', '法官', '审判长', '审判员', '书记员', '头部', '头部2', '当事人', '当事人2', '庭审程序说明',\n",
      "       '庭审程序说明2', '庭审过程', '庭审过程2', '庭审过程3', '庭审过程4', '庭审过程5', '庭审过程6', '法院意见',\n",
      "       '法院意见2', '判决结果', '判决结果2', '庭后告知', '庭后告知2', '结尾', '结尾2', '附录', '附录2'],\n",
      "      dtype='object')\n",
      "请问需要分析哪一列的关键词词频？(输入关键词部分):法院意见\n",
      "-------------------------------------------\n",
      "以下为表格的预览:\n",
      "        0\n",
      "借贷关系    1\n",
      "元债务系    1\n",
      "最高人民法院  1\n",
      "是否需要保存该词频表？(输入Y/N):N\n",
      "-------------------------------------------\n",
      "以下为表格的预览:\n",
      "Empty DataFrame\n",
      "Columns: [0]\n",
      "Index: []\n",
      "是否需要保存该词频表？(输入Y/N):N\n"
     ]
    }
   ],
   "source": [
    "pattern_character = re.compile(\"[\\u4e00-\\u9fa5]+\")\n",
    "def find_specific_word(segment_method,corpus,limitation):\n",
    "    \"\"\"\n",
    "    搜索机构名/地名\n",
    "    param segment_method:查询地名，还是机构名\n",
    "    param corpus:查询怎样的文本\n",
    "    param limitation:限定词词性\n",
    "    \"\"\"\n",
    "    for sentence in corpus:\n",
    "        list_places = []\n",
    "        term_list = segment_method.seg(sentence)\n",
    "        for every_word in term_list:\n",
    "            if limitation in str(every_word):\n",
    "                if \"之日起\" not in str(every_word):\n",
    "                    list_places.append(str(every_word).strip(\"/nt/ns\"))\n",
    "        return list_places\n",
    "                    \n",
    "if __name__ == '__main__':\n",
    "    # ------ 导入函数开始\n",
    "    flag_input = int(input(\"你是在本机上操作？还是远程操作？(1:本机操作,2:远程操作)\"))\n",
    "    if flag_input == 1:\n",
    "        root = Tk() # 实例化TKinter窗口\n",
    "        root.withdraw() # 隐藏TKinter窗口\n",
    "        table_ready_to_eat = import_table(root)\n",
    "    elif flag_input == 2:\n",
    "        table_ready_to_eat = remote_select()\n",
    "    else:\n",
    "        logging.error(\"加载错误\")\n",
    "    # ------ 导入函数结束\n",
    "    \n",
    "    print(\"表格中所有的列:\",table_ready_to_eat.columns)\n",
    "    selected_column = input(\"请问需要分析哪一列的关键词词频？(输入关键词部分):\")\n",
    "    corpus = table_ready_to_eat[selected_column]\n",
    "    \n",
    "    results = list(filter(lambda x:isinstance(x,str),corpus))\n",
    "    segment_organization = HanLP.newSegment().enableOrganizationRecognize(True)\n",
    "    segment_place = HanLP.newSegment().enablePlaceRecognize(True)\n",
    "    organization = find_specific_word(segment_organization,results,\"/nt\")\n",
    "    places = find_specific_word(segment_place,results,\"/ns\")\n",
    "    organ_table = pd.DataFrame([dict(Counter(list(map(lambda x:re.search(pattern_character,x).group(),organization))))]).T\n",
    "    places_table = pd.DataFrame([dict(Counter(list(map(lambda x:re.search(pattern_character,x).group(),places))))]).T\n",
    "    save_model(organ_table)\n",
    "    save_model(places_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
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