185 lines
7.1 KiB
Plaintext
185 lines
7.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"旧题目数: 12858 , 新题目数: 191\n",
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"开始新题与旧题的比对\n",
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"50\n",
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"100\n",
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"150\n",
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"开始新题之间的比对\n",
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"50\n",
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"100\n",
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"150\n",
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"总耗时: 79.84281706809998 秒.\n",
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"发现相似: 252 , 其中已标注: 0 .\n"
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]
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}
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],
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"source": [
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"# from hashlib import new\n",
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"import os,re,difflib,Levenshtein,time,json\n",
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"\n",
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"# 重要!!! 新题目的范围\n",
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"id_new_problems = \"12138:12328\"\n",
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"threshold = 0.85\n",
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"\n",
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"#生成数码列表, 逗号分隔每个区块, 区块内部用:表示整数闭区间\n",
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"def generate_number_set(string):\n",
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" string = re.sub(r\"[\\n\\s]\",\"\",string)\n",
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" string_list = string.split(\",\")\n",
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" numbers_list = []\n",
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" for s in string_list:\n",
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" if not \":\" in s:\n",
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" numbers_list.append(s.zfill(6))\n",
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" else:\n",
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" start,end = s.split(\":\")\n",
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" for ind in range(int(start),int(end)+1):\n",
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" numbers_list.append(str(ind).zfill(6))\n",
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" return numbers_list\n",
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"\n",
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"#字符串预处理\n",
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"def pre_treating(string):\n",
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" string = re.sub(r\"\\\\begin\\{center\\}[\\s\\S]*?\\\\end\\{center\\}\",\"\",string)\n",
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" string = re.sub(r\"(bracket\\{\\d+\\})|(blank\\{\\d+\\})|(fourch)|(twoch)|(onech)\",\"\",string)\n",
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" string = re.sub(r\"[\\s\\\\\\{\\}\\$\\(\\)\\[\\]]\",\"\",string)\n",
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" string = re.sub(r\"[\\n\\t]\",\"\",string)\n",
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" string = re.sub(r\"(displaystyle)|(overrightarrow)\",\"\",string)\n",
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" string = re.sub(r\"[,\\.:;?]\",\"\",string)\n",
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" return string\n",
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"\n",
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"#difflab字符串比较\n",
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"def difflab_get_equal_rate(str1, str2):\n",
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" str1 = pre_treating(str1)\n",
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" str2 = pre_treating(str2)\n",
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" return difflib.SequenceMatcher(None, str1, str2).ratio()\n",
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"\n",
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"#Levenshtein jaro字符串比较\n",
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"def jaro_get_equal_rate(str1,str2):\n",
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" str1 = pre_treating(str1)\n",
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" str2 = pre_treating(str2)\n",
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" return Levenshtein.jaro(str1,str2)\n",
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"\n",
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"#Levenshtein 字符串比较\n",
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"def Lev_get_equal_rate(str1,str2):\n",
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" str1 = pre_treating(str1)\n",
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" str2 = pre_treating(str2)\n",
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" return Levenshtein.ratio(str1,str2)\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"#指定对比方法\n",
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"sim_test = jaro_get_equal_rate\n",
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"\n",
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"#读入题库\n",
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"with open(r\"../题库0.3/Problems.json\",\"r\",encoding = \"utf8\") as f:\n",
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" database = f.read()\n",
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"pro_dict = json.loads(database)\n",
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"\n",
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"#生成旧题目数据库字典与新题目数据库字典\n",
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"new_id_list = generate_number_set(id_new_problems)\n",
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"old_problems_dict = {}\n",
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"new_problems_dict = {}\n",
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"for id in pro_dict:\n",
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" if id in new_id_list:\n",
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" new_problems_dict[id] = pro_dict[id]\n",
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" else:\n",
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" old_problems_dict[id] = pro_dict[id]\n",
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"print(\"旧题目数:\",len(old_problems_dict),\", 新题目数:\",len(new_problems_dict))\n",
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"\n",
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"#记录起始时间\n",
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"start_time = time.time()\n",
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"suspect_count = 0\n",
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"remarked = 0\n",
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"\n",
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"alike_problems = \"\"\n",
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"\n",
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"#开始新题与旧题的比对\n",
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"count = 0\n",
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"print(\"开始新题与旧题的比对\")\n",
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"for id_new in new_problems_dict:\n",
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" count += 1\n",
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" if count % 50 == 0:\n",
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" print(count)\n",
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" for id_old in old_problems_dict:\n",
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" similar_rate = sim_test(new_problems_dict[id_new][\"content\"],old_problems_dict[id_old][\"content\"])\n",
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" if similar_rate > threshold or id_new in old_problems_dict[id_old][\"related\"] or id_new in old_problems_dict[id_old][\"same\"] or id_old in new_problems_dict[id_new][\"related\"] or id_old in new_problems_dict[id_new][\"same\"]:\n",
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" suspect_count += 1\n",
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" if not (id_new in old_problems_dict[id_old][\"related\"] or id_new in old_problems_dict[id_old][\"same\"] or id_old in new_problems_dict[id_new][\"related\"] or id_old in new_problems_dict[id_new][\"same\"]):\n",
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" alike_problems += (\"%.4f\" %similar_rate) + \"\\n\\n\" + id_new + \" \" + new_problems_dict[id_new][\"content\"] + \"\\n\\n\" + id_old + \" \" + old_problems_dict[id_old][\"content\"] + \"\\n\\n\"\n",
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" else:\n",
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" remarked += 1\n",
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"\n",
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"#开始新题之间的比对\n",
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"count = 0\n",
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"print(\"开始新题之间的比对\")\n",
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"while len(new_problems_dict) >= 2:\n",
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" count += 1\n",
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" if count % 50 == 0:\n",
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" print(count)\n",
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" keys = list(new_problems_dict.keys())\n",
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" current_problem = new_problems_dict.pop(keys[0])\n",
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" for id_new in new_problems_dict:\n",
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" similar_rate = sim_test(new_problems_dict[id_new][\"content\"],current_problem[\"content\"])\n",
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" if similar_rate > threshold or id_new in current_problem[\"related\"] or id_new in current_problem[\"same\"] or current_problem[\"id\"] in new_problems_dict[id_new][\"related\"] or current_problem[\"id\"] in new_problems_dict[id_new][\"same\"]:\n",
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" suspect_count += 1\n",
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" if not (id_new in current_problem[\"related\"] or id_new in current_problem[\"same\"] or current_problem[\"id\"] in new_problems_dict[id_new][\"related\"] or current_problem[\"id\"] in new_problems_dict[id_new][\"same\"]):\n",
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" alike_problems += (\"%.4f\" %similar_rate) + \"\\n\\n\" + id_new + \" \" + new_problems_dict[id_new][\"content\"] + \"\\n\\n\" + current_problem[\"id\"] + \" \" + current_problem[\"content\"] + \"\\n\\n\"\n",
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" else:\n",
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" remarked += 1\n",
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"\n",
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"\n",
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"#记录终止时间及显示结果\n",
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"end_time = time.time()\n",
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"print(\"总耗时:\",end_time-start_time,\"秒.\")\n",
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"print(\"发现相似: \",suspect_count,\", 其中已标注: \",remarked,\".\")\n",
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"\n",
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"with open(\"临时文件/相似题目.txt\",\"w\",encoding=\"utf8\") as f:\n",
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" f.write(alike_problems)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"kernelspec": {
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"display_name": "Python 3.9.13 ('base')",
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"language": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13 (main, Aug 25 2022, 23:51:50) [MSC v.1916 64 bit (AMD64)]"
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"orig_nbformat": 4,
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"vscode": {
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