舆情预测函数定义

utils/getEchartsData.py

@@ -128,12 +128,12 @@ def getIPCharByCommentsRegion():
 def getCommentCharDataOne():
     xData = []
     rangeNum = 20
-    for item in range(1,100):
+    for item in range(100):
         xData.append(str(rangeNum * item) + '-' + str(rangeNum * (item + 1)))
     yData = [0 for x in range(len(xData))]
     for comment in commentList:
-        for item in range(99):
-            if int(comment[2]) < rangeNum * (item + 2):
+        for item in range(100):
+            if int(comment[2]) < rangeNum * (item + 1):
                 yData[item] += 1
                 break
     return xData,yData

utils/predict_demo1.py

@@ -0,0 +1,47 @@
+import numpy as np
+import datetime
+import matplotlib.pyplot as plt
+
+
+def datetime_to_number(date: str):  # 格式化日期转换为 integer
+    date_number = datetime.datetime.strptime(date, "%Y-%m-%d")
+    base_number = datetime.datetime.strptime("2024-1-1", "%Y-%m-%d")
+    return (date_number - base_number).days
+
+
+def predict_future_values(data):
+    # 提取并排序日期
+    sorted_dates = sorted(data.keys(), key=lambda date: datetime.datetime.strptime(date, "%Y-%m-%d"))
+    sorted_data = {k: data[k] for k in sorted_dates}
+
+    # 将日期转换为整数并提取相应的值
+    xs = np.array([datetime_to_number(date) for date in sorted_data.keys()])
+    ys = np.array([data[date] for date in sorted_data.keys()])
+
+    # 拟合线性回归模型
+    fit = np.polyfit(xs, ys, 1)
+    fn = np.poly1d(fit)
+
+    # 获取最新日期，并生成未来三天的日期
+    latest_date = sorted_dates[-1]
+    latest_date_obj = datetime.datetime.strptime(latest_date, "%Y-%m-%d")
+    future_dates = [(latest_date_obj + datetime.timedelta(days=i)).strftime("%Y-%m-%d") for i in range(1, 6)]
+
+    # 预测未来日期的值
+    predictions = {}
+    for date in future_dates:
+        date_num = datetime_to_number(date)
+        if int(fn(date_num))<=0:
+            predictions[date] = 0
+        else:
+            predictions[date] = int(fn(date_num))
+
+    return predictions
+
+
+if __name__ == '__main__':
+    data = {'2024-06-15': 1, '2024-06-18': 1, '2024-06-22': 1, '2024-06-23': 1, '2024-07-01': 3, '2024-07-02': 4, '2024-07-03': 4, '2024-07-04': 14}
+    predictions = predict_future_values(data)
+    print(predictions)
+    # for date, value in predictions.items():
+    #     print(f'{date} PREDICTION: {value}')

utils/yuqingpredict.py

@@ -0,0 +1,93 @@
+from utils.getPublicData 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
+    xData = top_10_topics['name'].tolist()
+    yData = top_10_topics['value'].tolist()
+    return xData, yData
+
+def getTopicPageCreatedAtCharData(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
+    sorted_data = {k: createdAt[k] for k in sorted(createdAt, key=lambda date: datetime.datetime.strptime(date, "%Y-%m-%d"))}
+    return topic,sorted_data
+    # return topic,list(createdAt.keys()),list(createdAt.values())
+    # return topic, createdAt.items()
+
+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(getTopicPageCreatedAtCharData("生活"))