Merge pull request #7 from wjhgq/main

The new practice sequence model to complete the public opinion prediction function.

 import numpy as np
 import datetime
-import matplotlib.pyplot as plt
+import pandas as pd
+from pmdarima import auto_arima
 
-
-def datetime_to_number(date: str):  # 格式化日期转换为 integer
+def datetime_to_number(date: str):
+    """Convert a date string 'YYYY-MM-DD' to a relative day number."""
     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, forecast_days=5):
+    """
+    Use auto_arima from pmdarima to fit a suitable ARIMA/SARIMA model for the time series,
+    then predict future values for the specified number of 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()])
+    Parameters:
+    data: dict, keys are date strings 'YYYY-MM-DD', values are integer counts
+    forecast_days: int, number of days to predict into the future
 
-    # 拟合线性回归模型
-    fit = np.polyfit(xs, ys, 1)
-    fn = np.poly1d(fit)
+    Returns:
+    predictions: dict, keys are future date strings 'YYYY-MM-DD', values are predicted integers (≥0)
+    """
+    if not data:
+        return {}
 
-    # 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)]
+    # Sort data by date
+    sorted_dates = sorted(data.keys(), key=lambda d: datetime.datetime.strptime(d, "%Y-%m-%d"))
+    start_date = sorted_dates[0]
+    end_date = sorted_dates[-1]
+    
+    # Create a full date range to ensure continuity in the time series
+    full_range = pd.date_range(start=start_date, end=end_date, freq='D')
+    ts = pd.Series(0, index=full_range, dtype=float)
+    for d in data:
+        ts[pd.to_datetime(d)] = data[d]
 
-    # 预测未来日期的值
+    # Simple smoothing: optional step to reduce noise (moving average over 3 days)
+    # This is a mild smoothing to handle noisy data. You can comment this out if not needed.
+    ts_smoothed = ts.rolling(window=3, min_periods=1).mean()
+    
+    # Fit the time series with auto_arima to find the best parameters
+    model = auto_arima(ts_smoothed, 
+                       start_p=1, start_q=1, 
+                       max_p=5, max_q=5, 
+                       seasonal=False,
+                       trace=False, error_action='ignore', suppress_warnings=True, stepwise=True)
+    
+    # Predict the future values
+    forecast = model.predict(n_periods=forecast_days)
+    # Construct future dates
+    last_date = pd.to_datetime(end_date)
+    future_dates = [last_date + datetime.timedelta(days=i) for i in range(1, forecast_days+1)]
+    
+    # Convert forecast results to dict with non-negative integers
     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))
+    for d, v in zip(future_dates, forecast):
+        predictions[d.strftime("%Y-%m-%d")] = max(int(round(v)), 0)
 
     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}')
+    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
+    }
+    preds = predict_future_values(data)
+    print(preds)

 from utils.getPublicData import *
-from utils.predict import *
-articleList = getAllArticleData()
-commentList = getAllCommentsData()
+from utils.predict import predict_future_values  # Use the new function
 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
+import pandas as pd
 
-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_dict = sorted(merged_dict.items(), key=lambda item: item[1], reverse=True)
-    merged_list = [[key, str(value)] for key, value in merged_dict]
-    return merged_list
-def getAllTopicData():
-    # 读取合并文件 merge.csv
-    # data = []
-    # df = pd.read_csv('./merged_topics.csv',encoding='utf8')
-    # for i in df.values:
-    #     try:
-    #         data.append([
-    #             re.search('[\u4e00-\u9fa5]+',str(i)).group(),
-    #             re.search('\d+',str(i)).group()
-    #         ])
-    #     except:
-    #         continue
-    return mergeTopics(getTopicByArticle(), getTopicByComments())
-
-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
+def getTopicCreatedAtandpredictData(topic):
     createdAt = {}
     for i in articleList:
         if i[14]==topic:
@@ -75,30 +19,13 @@ def getTopicCreatedAtandpredictData(topic):# 统计特定话题的评论在每
                 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
-    print(list(createdAt.keys()),list(createdAt.values()))
-    return list(createdAt.keys()),list(createdAt.values())
 
-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
-    # print(mergeTopics(getTopicByArticle(), getTopicByComments()))
-    # writeTopicsToCSV(merged_topics, 'merged_topics.csv')
-    print(getAllTopicData())
+    # Use the improved time series prediction approach
+    predictions = predict_future_values(createdAt, forecast_days=5)
+
+    # Merge historical data and predictions
+    combined_data = {**createdAt, **predictions}
+    combined_data = {k: combined_data[k] for k in sorted(combined_data, key=lambda date: datetime.datetime.strptime(date, "%Y-%m-%d"))}
 
+    print(list(combined_data.keys()), list(combined_data.values()))
+    return list(combined_data.keys()), list(combined_data.values())