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# 导入需要的工具包
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from py2neo import Graph, Node, Relationship, NodeMatcher, Subgraph
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import pandas as pd
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import numpy as np
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import os
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import sys
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sys.path.append(os.getcwd())
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import json
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from config import neo4j_url,neo4j_username,neo4j_password
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from config import kg_data
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from config import study_path_data
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from pprint import pprint as pp
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# 连接Neo4j数据库
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graph = Graph(neo4j_url, auth=(neo4j_username,neo4j_password) )
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# 获取学生行为表
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# 获取学生闯关情况表
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games = pd.read_csv(study_path_data+'games.csv',sep='\t')
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# 获取学生作业情况表
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student_works = pd.read_csv(study_path_data+'student_works.csv',sep='\t')
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#获取学生观看教学视频情况表
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watch_course_video = pd.read_csv(study_path_data+'watch_course_videos.csv',sep='\t')
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# 获取学生访问课件表
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attachment_logs = pd.read_csv(study_path_data+'attachment_logs.csv',sep='\t')
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def get_review_path(course_id, student_id, platform):
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#根据course_id查找知识森林中的节点，判断是否存在
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course_query = "match(c:`教学课堂`) where c.course_id='{}' return c.course_id".format(course_id)
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course_result = graph.run(course_query).data()
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# 判断course_id是否存在，如果存在，进行如下查询操作，返回查询结果；如不存在，返回空值
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if course_result:
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# 根据course_id查询知识森林实训的相关节点
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shixun_query = '''match(c:`教学课堂`) where c.course_id="{}" match(c)-[:`课堂课程`]->(s:`实践课程`)
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match(s)-[:`课程章节`]->(st:`章节`) match(st)-[:`章节知识点`]->(kn:`知识点`)
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match(kn)-[:`知识点实训`]->(shixun:`实训`) return st.stage_id as stage_id,st.stage_name as stage_name,
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st.stage_sort as stage_sort,kn.knowledge_id as knowledge_id,kn.knowledge as knowledge,
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shixun.shixun_id as shixun_id,shixun.shixun_name as shixun_name,shixun.link as shixun_link,
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count(shixun.shixun_id) as shixun_num'''.format(course_id)
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# 获取知识森林查询的实训信息
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shixun = graph.run(shixun_query).data()
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#判断查询结果是否为空，若非空进行如下筛选工作；若空值，赋值相同结构为空值的dataframe格式表
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if shixun:
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# 将查询结果转化为dataframe格式
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shixun_data = pd.DataFrame(shixun)
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# 分析学生关卡闯关情况
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# 筛选指定学生student_id
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stu_games = games[games['user_id']==int(student_id)]
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# 根据学生闯关的准确率获取通关关卡记录
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finish_games = stu_games[stu_games['accuracy']>=0.6]
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# 根据学生闯关的准确率获取未通关关卡记录
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not_finish_games = stu_games[stu_games['accuracy']<0.6]
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# 获取通关关卡对应的实训集合
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stu_finish_game = set(finish_games['shixun_id'])
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# 获取未通关关卡对应的实训集合
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stu_not_finish_game = set(not_finish_games['shixun_id'])
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# 获取学生完成实训集合
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stu_finish_shixun = stu_finish_game-stu_not_finish_game
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# 分析学生作业完成情况
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# 筛选指定学生student_id
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stu_homestudent_works = student_works[student_works['user_id']==int(student_id)]
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# 快速筛选未完成的作业记录
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stu_homestudent_works = stu_homestudent_works[stu_homestudent_works['final_score']<=0.6]
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# stu_homestudent_works = stu_homestudent_works[stu_homestudent_works['cost_time']<=100]
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# 获取未完成作业对应的实训集合
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stu_not_finish_homework = set(stu_homestudent_works['shixun_id'])
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stu_finish_shixun = stu_finish_shixun-stu_not_finish_homework
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#快速筛选掉学生已完成的实训，保留学生未完成的实训
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shixun_data = shixun_data[~shixun_data['shixun_id'].isin(stu_finish_shixun)]
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else:
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shixun_data = pd.DataFrame(columns=["stage_id","stage_name","stage_sort","knowledge_id","knowledge",
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"shixun_id","shixun_name","shixun_link","shixun_num"])
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#知识森林查询教学视频相关节点
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video_query = '''match(c:`教学课堂`) where c.course_id="{}" match(c)-[:`课堂课程`]->(s:`实践课程`)
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match(s)-[:`课程章节`]->(st:`章节`) match(st)-[:`章节知识点`]->(kn:`知识点`)
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match(kn)-[:`知识点视频`]->(video:`教学视频`) return st.stage_id as stage_id,
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st.stage_name as stage_name,st.stage_sort as stage_sort,kn.knowledge_id as knowledge_id,
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kn.knowledge as knowledge,video.video_item_id as video_id,video.video_name as video_name,
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video.link as video_link,count(video.video_item_id) as video_num'''.format(course_id)
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#获取知识森林查询的教学视频信息
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video = graph.run(video_query).data()
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if video:
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# 将查询的结果转换为dataframe格式
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video_data=pd.DataFrame(video)
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# 分析学生视频学习情况
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stu_videos = watch_course_video[watch_course_video['user_id']==int(student_id)]
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finish_videos = stu_videos[stu_videos['is_finished']==1]
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# 获取学生完成教学视频的ID集合
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stu_video = set(finish_videos['video_item_id'])
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# 快速筛选掉学生已完成的教学视频
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video_data = video_data[~video_data['video_id'].isin(stu_video)]
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else:
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# 如果没有查询到视频信息，则返回空dataframe
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video_data = pd.DataFrame(columns=["stage_id","stage_name","knowledge_id","knowledge","video_id",
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"video_name","video_link","video_num"])
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# 知识森林查询教学课件相关节点
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att_query = '''match(c:`教学课堂`) where c.course_id="{}" match(c)-[:`课堂课程`]->(s:`实践课程`)
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match(s)-[:`课程章节`]->(st:`章节`) match(st)-[:`章节知识点`]->(kn:`知识点`)
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match(kn)-[:`知识点课件`]->(att:`课件`) return st.stage_id as stage_id,st.stage_name as stage_name,
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st.stage_sort as stage_sort,kn.knowledge_id as knowledge_id,kn.knowledge as knowledge,
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att.attachment_id as attachment_id, att.filename as filename,att.link as attachment_link,
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count(att.attachment_id) as attachment_num'''.format(course_id)
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att = graph.run(att_query).data()
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if att:
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att_data = pd.DataFrame(att)
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#分析学生访问课件学习情况
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stu_att = attachment_logs[attachment_logs['user_id']==int(student_id)]
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finish_atts = stu_att[stu_att['visits']>1]
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att_set = set(finish_atts['attachment_id'])
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#快速筛选掉学生已完成的教学课件
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att_data = att_data[~att_data['attachment_id'].isin(att_set)]
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else:
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att_data = pd.DataFrame(columns=["stage_id","stage_name","stage_sort","knowledge_id","knowledge",
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"attachment_id","filename","attachment_link"])
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merged_data = pd.merge(shixun_data,video_data,on=['stage_id','stage_name',"stage_sort",'knowledge_id',
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'knowledge'],how='outer')
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merged_data = pd.merge(merged_data,att_data,on=['stage_id','stage_name',"stage_sort",'knowledge_id',
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'knowledge'],how='outer')
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merged_data['shixun_num'].fillna(0,inplace=True)
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merged_data['video_num'].fillna(0,inplace=True)
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merged_data['video_num'].fillna(0,inplace=True)
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if platform == '1':
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merged_data['attachment_link'] = merged_data['attachment_link'].str.replace('www.', 'ilearning.')
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merged_data['shixun_link'] = merged_data['shixun_link'].str.replace('www.', 'ilearning.')
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merged_data['video_link'] = merged_data['video_link'].str.replace('www.', 'ilearning.')
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shixun = (merged_data.groupby(['stage_id','stage_name',"stage_sort",'knowledge_id','knowledge',
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'shixun_num','video_num'])
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.apply(lambda x:x[['shixun_id','shixun_name', 'shixun_link']].to_dict('r'))
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.reset_index(name='shixun'))
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video = (merged_data.groupby(['stage_id','stage_name',"stage_sort",'knowledge_id','knowledge',
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'shixun_num','video_num'])
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.apply(lambda x:x[['video_id','video_name', 'video_link']].to_dict('r'))
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.reset_index(name='video'))
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att = (merged_data.groupby(['stage_id','stage_name',"stage_sort",'knowledge_id','knowledge',
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'shixun_num','video_num'])
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.apply(lambda x:x[['attachment_id','filename', 'attachment_link']].to_dict('r'))
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.reset_index(name='attachment'))
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linshi = shixun.merge(video,how='outer',on=['stage_id','stage_name',"stage_sort",'knowledge_id',
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'knowledge','shixun_num','video_num'])
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res=linshi.merge(att,how='outer',on=['stage_id','stage_name',"stage_sort",'knowledge_id',
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'knowledge','shixun_num','video_num'])
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result = (res.groupby(['stage_id','stage_name',"stage_sort"]).apply(lambda x :x[['knowledge_id','knowledge','shixun_num','video_num','shixun','video','attachment']].to_dict('r'))
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.reset_index(name='knowledge'))
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result=json.loads(result.to_json(orient='records',force_ascii=False))
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return result
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else:
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# 如果course_id不存在，返回空值
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return {}
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if __name__ == '__main__':
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# 读取数据
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course_id = "24002"
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student_id = '2383'
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result = get_review_path(course_id,student_id)
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print(result) |
