juanboy
105 lines
3.8 KiB
Python
105 lines
3.8 KiB
Python
from utils.getPublicData import *
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from utils.predict import *
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articleList = getAllArticleData()
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commentList = getAllCommentsData()
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import csv
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import os
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import datetime
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def getTopicByArticle():# 返回文章内容的话题字典
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articleTopicDic = {}
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for i in articleList:
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if i[14] != None:
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if i[14] in articleTopicDic.keys():
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articleTopicDic[i[14]] += 1
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else:
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articleTopicDic[i[14]] = 1
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resultData = []
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for key,value in articleTopicDic.items():
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resultData.append({
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'name':key,
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'value':value
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})
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return resultData
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def getTopicByComments():# 返回评论内容的话题字典
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commentsTopicDic = {}
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for i in commentList:
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if i[9] != None:
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if i[9] in commentsTopicDic:
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commentsTopicDic[i[9]] += 1
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else:
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commentsTopicDic[i[9]] = 1
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resultData = []
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for key,value in commentsTopicDic.items():
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resultData.append({
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'name':key,
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'value':value
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})
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return resultData
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def mergeTopics(article_topics, comment_topics):# 合并话题
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merged_dict = {}
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for topic in article_topics + comment_topics:
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if topic['name'] in merged_dict:
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merged_dict[topic['name']] += topic['value']
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else:
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merged_dict[topic['name']] = topic['value']
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merged_dict = sorted(merged_dict.items(), key=lambda item: item[1], reverse=True)
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merged_list = [[key, str(value)] for key, value in merged_dict]
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return merged_list
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def getAllTopicData():
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# 读取合并文件 merge.csv
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# data = []
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# df = pd.read_csv('./merged_topics.csv',encoding='utf8')
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# for i in df.values:
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# try:
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# data.append([
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# re.search('[\u4e00-\u9fa5]+',str(i)).group(),
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# re.search('\d+',str(i)).group()
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# ])
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# except:
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# continue
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return mergeTopics(getTopicByArticle(), getTopicByComments())
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def getTopicCreatedAtandpredictData(topic):# 统计特定话题的评论在每个日期的数量，并返回日期和对应的评论数量
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createdAt = {}
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for i in articleList:
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if i[14]==topic:
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if i[7] in createdAt.keys():
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createdAt[i[7]] += 1
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else:
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createdAt[i[7]] = 1
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for i in commentList:
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if i[9]==topic:
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if i[1] in createdAt.keys():
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createdAt[i[1]] += 1
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else:
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createdAt[i[1]] = 1
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createdAt = {k: createdAt[k] for k in sorted(createdAt, key=lambda date: datetime.datetime.strptime(date, "%Y-%m-%d"))}
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createdAt.update(predict_future_values(createdAt))
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sorted_data = {k: createdAt[k] for k in sorted(createdAt, key=lambda date: datetime.datetime.strptime(date, "%Y-%m-%d"))}
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# result_list = [0] * (len(sorted_data) - 5) + [1] * 5
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print(list(createdAt.keys()),list(createdAt.values()))
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return list(createdAt.keys()),list(createdAt.values())
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def writeTopicsToCSV(topics, file_name):
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# 检查文件是否存在，如果存在则附加写入，否则新建一个
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file_exists = os.path.isfile(file_name)
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# 按值的降序排序
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sorted_topics = sorted(topics, key=lambda x: x['value'], reverse=True)
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with open(file_name, 'w', newline='', encoding='utf-8') as csvfile:
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fieldnames = ['name', 'value']
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writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
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# 如果文件不存在，则写入表头
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if not file_exists:
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writer.writeheader()
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# 写入数据
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for topic in sorted_topics:
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writer.writerow(topic)
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if __name__ == '__main__':
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# 将话题数据写入 CSV 文件
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# print(mergeTopics(getTopicByArticle(), getTopicByComments()))
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# writeTopicsToCSV(merged_topics, 'merged_topics.csv')
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print(getAllTopicData())
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