yuqingpredict.py
3.62 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
from utils.getPublicData import *
from utils.predict import *
articleList = getAllArticleData()
commentList = getAllCommentsData()
import csv
import os
import datetime
def getTopicByArticle():# 返回文章内容的话题字典
articleTopicDic = {}
for i in articleList:
if i[14] != None:
if i[14] in articleTopicDic.keys():
articleTopicDic[i[14]] += 1
else:
articleTopicDic[i[14]] = 1
resultData = []
for key,value in articleTopicDic.items():
resultData.append({
'name':key,
'value':value
})
return resultData
def getTopicByComments():# 返回评论内容的话题字典
commentsTopicDic = {}
for i in commentList:
if i[9] != None:
if i[9] in commentsTopicDic:
commentsTopicDic[i[9]] += 1
else:
commentsTopicDic[i[9]] = 1
resultData = []
for key,value in commentsTopicDic.items():
resultData.append({
'name':key,
'value':value
})
return resultData
def mergeTopics(article_topics, comment_topics):# 合并话题
merged_dict = {}
for topic in article_topics + comment_topics:
if topic['name'] in merged_dict:
merged_dict[topic['name']] += topic['value']
else:
merged_dict[topic['name']] = topic['value']
merged_list = [{'name': key, 'value': value} for key, value in merged_dict.items()]
return merged_list
def getTopicData():
# 读取合并文件 merge.csv # 取前十个话题
top_10_topics = pd.read_csv('./merged_topics.csv').head(10)
# 获取话题名称和对应的值
X = top_10_topics['name'].tolist()
Y = top_10_topics['value'].tolist()
return X, Y
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
createdAt = {}
for i in articleList:
if i[14]==topic:
if i[7] in createdAt.keys():
createdAt[i[7]] += 1
else:
createdAt[i[7]] = 1
for i in commentList:
if i[9]==topic:
if i[1] in createdAt.keys():
createdAt[i[1]] += 1
else:
createdAt[i[1]] = 1
createdAt = {k: createdAt[k] for k in sorted(createdAt, key=lambda date: datetime.datetime.strptime(date, "%Y-%m-%d"))}
createdAt.update(predict_future_values(createdAt))
sorted_data = {k: createdAt[k] for k in sorted(createdAt, key=lambda date: datetime.datetime.strptime(date, "%Y-%m-%d"))}
result_list = [0] * (len(sorted_data) - 5) + [1] * 5
return topic,sorted_data,result_list
# return topic,list(createdAt.keys()),list(createdAt.values()),result_list
def writeTopicsToCSV(topics, file_name):
# 检查文件是否存在，如果存在则附加写入，否则新建一个
file_exists = os.path.isfile(file_name)
# 按值的降序排序
sorted_topics = sorted(topics, key=lambda x: x['value'], reverse=True)
with open(file_name, 'w', newline='', encoding='utf-8') as csvfile:
fieldnames = ['name', 'value']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
# 如果文件不存在，则写入表头
if not file_exists:
writer.writeheader()
# 写入数据
for topic in sorted_topics:
writer.writerow(topic)
if __name__ == '__main__':
# 将话题数据写入 CSV 文件
# merged_topics = mergeTopics(getTopicByArticle(), getTopicByComments())
# writeTopicsToCSV(merged_topics, 'merged_topics.csv')
print(getTopicCreatedAtandpredictData("生活"))