From 50797d0121bf04491f2cf3145869861141a36fb3 Mon Sep 17 00:00:00 2001 From: workpc Date: Tue, 31 Dec 2024 15:27:59 +0800 Subject: [PATCH] =?UTF-8?q?=E9=9B=8D=E5=AE=89=E7=8E=AF=E5=A2=83=E4=BD=BF?= =?UTF-8?q?=E7=94=A85=E4=B8=AA=E6=A8=A1=E5=9E=8B=E7=94=BB=E9=A2=84?= =?UTF-8?q?=E6=B5=8B=E5=9B=BE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- config_jingbo.py | 2 +- config_yongan.py | 4 +- lib/dataread.py | 5 +- lib/dataread_jingbo_pro.py | 1733 ----------------------------- lib/dataread_yongan.py | 1733 ----------------------------- main_yongan.py | 6 +- models/nerulforcastmodels.py | 407 +++++++ yongandataset/jbsh_yuanyou.db | Bin 741376 -> 741376 bytes yongandataset/历史价格-预测值.png | Bin 176592 -> 147020 bytes 9 files changed, 416 insertions(+), 3474 deletions(-) delete mode 100644 lib/dataread_jingbo_pro.py delete mode 100644 lib/dataread_yongan.py diff --git a/config_jingbo.py b/config_jingbo.py index 6a2b80d..3e903b5 100644 --- a/config_jingbo.py +++ b/config_jingbo.py @@ -176,7 +176,7 @@ table_name = 'v_tbl_crude_oil_warning' ### 开关 is_train = False # 是否训练 is_debug = False # 是否调试 -is_eta = False # 是否使用eta接口 +is_eta = True # 是否使用eta接口 is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效 is_timefurture = True # 是否使用时间特征 is_fivemodels = False # 是否使用之前保存的最佳的5个模型 diff --git a/config_yongan.py b/config_yongan.py index 7609edc..72050b6 100644 --- a/config_yongan.py +++ b/config_yongan.py @@ -107,7 +107,6 @@ ClassifyId = 1214 ################################################################################################################ 变量定义--雍安测试环境 login_pushreport_url = "http://192.168.100.115:9090/dom-api/api/server/login" upload_url = "http://192.168.100.115:9090/dom-api/api/analysis/reportInfo/researchUploadReportSave" -# upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei upload_warning_url = "http://192.168.100.115:9090/dom-api/api/basicBuiness/crudeOilWarning/save" query_data_list_item_nos_url = "http://192.168.100.115:9090/dom-api/api/warehouse/dwDataItem/queryDataListItemNos" @@ -169,7 +168,8 @@ dbusername ='root' password = '123456' dbname = 'jingbo_test' table_name = 'v_tbl_crude_oil_warning' - +# 表名前缀 +table_name_prefix = 'yongan_' ### 开关 is_train = False # 是否训练 diff --git a/lib/dataread.py b/lib/dataread.py index 108642b..8eb14c0 100644 --- a/lib/dataread.py +++ b/lib/dataread.py @@ -41,8 +41,9 @@ plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 # from config_jingbo_pro import * -from config_jingbo import * -# from config_juxiting import * +# from config_jingbo import * +# from config_yongan import * +from config_juxiting import * diff --git a/lib/dataread_jingbo_pro.py b/lib/dataread_jingbo_pro.py deleted file mode 100644 index 07e1d87..0000000 --- a/lib/dataread_jingbo_pro.py +++ /dev/null @@ -1,1733 +0,0 @@ - -# 导入模块 -import pandas as pd -import numpy as np -import datetime -import string -import base64 -import requests -import random -import time -import re -import os -import hmac -import hashlib -import json -import math -import torch -torch.set_float32_matmul_precision("high") -import matplotlib.pyplot as plt -#设置plt显示中文 -plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 -plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 - -from datetime import timedelta -from sklearn import metrics -from reportlab.pdfbase import pdfmetrics # 注册字体 -from reportlab.pdfbase.ttfonts import TTFont # 字体类 -from reportlab.platypus import Table, SimpleDocTemplate, Paragraph, Image # 报告内容相关类 -from reportlab.lib.pagesizes import letter # 页面的标志尺寸(8.5*inch, 11*inch) -from reportlab.lib.styles import getSampleStyleSheet # 文本样式 -from reportlab.lib import colors # 颜色模块 -from reportlab.graphics.charts.barcharts import VerticalBarChart # 图表类 -from reportlab.graphics.charts.legends import Legend # 图例类 -from reportlab.graphics.shapes import Drawing # 绘图工具 -from reportlab.lib.units import cm # 单位：cm - -# 注册字体(提前准备好字体文件, 如果同一个文件需要多种字体可以注册多个) -pdfmetrics.registerFont(TTFont('SimSun', 'SimSun.ttf')) -#设置plt显示中文 -plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 -plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 - - -from config_jingbo_pro import * - - - - -# 定义函数 -def loadcsv(filename): - """ - 读取指定文件名的 CSV 文件。 - 如果文件编码为 UTF-8，则使用 UTF-8 编码读取；否则，使用 GBK 编码读取。 - - 参数: - filename (str): 要读取的 CSV 文件的文件名。 - - 返回: - pandas.DataFrame: 读取的数据。 - - """ - # 读取csv文件 - try: - df = pd.read_csv(filename, encoding='utf-8') - except UnicodeDecodeError: - df = pd.read_csv(filename, encoding='gbk') - return df - - -def dateConvert(df, datecol='ds'): - """ - 将数据框 df 中的 datecol 列转换为日期时间类型。 - - 参数: - df (pandas.DataFrame): 要转换的 DataFrame。 - datecol (str): 要转换的列名，默认为 'ds'。 - - 返回: - pandas.DataFrame: 转换后的 DataFrame。 - - """ - # 将date列转换为datetime类型 - try: - df[datecol] = pd.to_datetime(df[datecol],format=r'%Y-%m-%d') - except: - df[datecol] = pd.to_datetime(df[datecol],format=r'%Y/%m/%d') - return df - - -def calculate_kdj(data, n=9): - ''' - 给传进来的df 添加列： 波动率，最高，最低，k,d ，j - ''' - # 对数据按照日期升序排序 - data = data.sort_values(by='ds', ascending=True) - # 因为没有高开低价格，利用每日波动率模拟当天最高价和最低价 - data['pctchange'] = data['y'].pct_change() - # 收益为0的用0.01 - data['pctchange'] = data['pctchange'].replace(0,0.01) - # 去除空值 - data.dropna(inplace=True) - # 重置索引 - data.reset_index(drop=True,inplace=True) - # 计算最高价和最低价 - data['high'] = data['y']* (1+abs(data['pctchange'])/2) - data['low'] = data['y']* (1-abs(data['pctchange'])/2) - # 计算n日内最低价 - low_list = data['y'].rolling(window=n, min_periods=1).min() - # 计算n日内最高价 - high_list = data['y'].rolling(window=n, min_periods=1).max() - # 计算未成熟随机值 - rsv = ((data['y'] - low_list) / (high_list - low_list)) * 100 - # 初始化k值为50 - k = pd.Series(50, index=data.index) - # 初始化d值为50 - d = pd.Series(50, index=data.index) - # 计算k值和d值 - for i in range(1, len(data)): - k[i] = (2/3 * k[i - 1]) + (1/3 * rsv[i]) - d[i] = (2/3 * d[i - 1]) + (1/3 * k[i]) - # 计算j值 - j = 3 * k - 2 * d - - # 将k值、d值和j值添加到数据中 - data['K'] = k - data['D'] = d - data['J'] = j - # 将包含 KDJ 指标的数据保存到新的 CSV 文件 - data.to_csv('stock_data_with_kdj.csv', index=False) - # data = data.dropna() - return data - - -# 上传报告 -def get_head_auth_report(): - """ - 通过 POST 请求登录到指定的 URL，并从响应中获取认证令牌。 - - 返回: - str: 如果登录成功，返回认证令牌；否则返回 None。 - """ - logger.info("获取token中...") - logger.info(f'url:{login_pushreport_url},login_data:{login_data}') - # 发送 POST 请求到登录 URL，携带登录数据 - login_res = requests.post(url=login_pushreport_url, json=login_data, timeout=(3, 30)) - - # 将响应内容转换为 JSON 格式 - text = json.loads(login_res.text) - logger.info(f'token接口响应：{text}') - # 如果响应状态为成功 - if text["status"]: - # 从响应数据中获取认证令牌 - token = text["data"]["accessToken"] - # 返回认证令牌 - return token - - -def upload_report_data(token, upload_data): - """ - 上传报告数据到指定的URL - - 参数: - token (str): 认证令牌 - upload_data (dict): 要上传的报告数据，包含必要的字段和信息 - - 返回: - dict: 如果上传成功，返回响应对象；否则返回None - """ - # 直接使用传入的 upload_data - upload_data = upload_data - - # 设置请求头部 - headers = {"Authorization": token} - - # 打印日志，显示正在上传报告数据 - logger.info("报告上传中...") - - # 打印日志，显示认证头部信息 - logger.info(f"token:{token}") - - # 打印日志，显示要上传的报告数据 - logger.info(f"upload_data:{upload_data}" ) - - # 发送POST请求，上传报告数据 - upload_res = requests.post(url=upload_url, headers=headers, json=upload_data, timeout=(3, 15)) - - # 将响应内容转换为 JSON 格式 - upload_res = json.loads(upload_res.text) - - # 打印日志，显示响应内容 - logger.info(upload_res) - - # 如果上传成功，返回响应对象 - if upload_res: - return upload_res - # 如果上传失败，打印日志并返回None - else: - logger.info("报告上传失败") - return None - - -def upload_warning_data(warning_data): - """ - 上传预警数据到指定的URL - - 参数: - warning_data (dict): 要上传的预警数据，包含必要的字段和信息 - - 返回: - requests.Response: 如果上传成功，返回响应对象；否则返回None - """ - # 获取认证头部信息 - token = get_head_auth_report() - - # 设置请求头部 - headers = {"Authorization": token} - - # 打印日志，显示正在上传预警数据 - logger.info("预警上传中...") - - # 打印日志，显示上传的URL - logger.info(f"upload_warning_url:{upload_warning_url}") - - # 打印日志，显示认证头部信息 - logger.info(f"token:{token}") - - # 打印日志，显示要上传的预警数据 - logger.info(f"warning_data:{warning_data}") - - # 发送POST请求，上传预警数据 - upload_res = requests.post(url=upload_warning_url, headers=headers, json=warning_data, timeout=(3, 15)) - - # 如果上传成功，返回响应对象 - if upload_res: - return upload_res - # 如果上传失败，打印日志并返回None - else: - logger.info("预警上传失败") - return None - - - -def upload_warning_info(df_count): - """ - 上传预警信息到指定的URL - - 参数: - df_count (int): 停更的数量 - - 返回: - None - """ - # 打印日志，显示正在上传预警信息 - logger.info(f'上传预警信息') - - try: - # 获取当前日期 - warning_date = datetime.datetime.now().strftime('%Y-%m-%d') - - # 构建预警内容 - content = f'{warning_date}有{df_count}个停更' - - # 更新预警数据中的日期和内容 - warning_data['data']['WARNING_DATE'] = warning_date - warning_data['data']['WARNING_CONTENT'] = content - - # 调用 upload_warning_data 函数上传预警数据 - upload_warning_data(warning_data) - - # 打印日志，显示上传预警信息成功 - logger.info(f'上传预警信息成功') - except Exception as e: - # 打印日志，显示上传预警信息失败，并记录异常信息 - logger.error(f'上传预警信息失败：{e}') - - - -def create_feature_last_update_time(df): - """ - 计算特征停更信息用 - 参数: - df (DataFrame): 包含特征数据的 DataFrame - 返回: - DataFrame: 包含特征停更信息的 DataFrame - str: y 列的最后更新时间 - """ - df1 = df.copy() - # 找到每列的最后更新时间 - df1.set_index('ds', inplace=True) - last_update_times = df1.apply(lambda x: x.dropna().index.max().strftime('%Y-%m-%d') if not x.dropna().empty else None) - - # 保存每列的最后更新时间到文件 - last_update_times_df = pd.DataFrame(columns = ['feature', 'last_update_time','is_value','update_period','warning_date','stop_update_period']) - - # 打印每列的最后更新时间 - for column, last_update_time in last_update_times.items(): - values = [] - # 判断是不是常数值 - if df1[column].tail(20).nunique() == 1: - values = values + [column, last_update_time,1] - else: - values = values + [column, last_update_time,0] - # 计算特征数据值的时间差 - try: - # 计算预警日期 - time_diff = (df1[column].dropna().index.to_series().diff().mode()[0]).total_seconds() / 3600 / 24 - last_update_time_datetime = datetime.datetime.strptime(last_update_time, '%Y-%m-%d') - last_update_date = end_time if end_time != '' else datetime.datetime.now().strftime('%Y-%m-%d') - end_time_datetime = datetime.datetime.strptime(last_update_date, '%Y-%m-%d') - early_warning_date = last_update_time_datetime + timedelta(days=time_diff)*2 + timedelta(days=1) - stop_update_period = int(math.ceil((end_time_datetime-last_update_time_datetime).days / time_diff)) - early_warning_date = early_warning_date.strftime('%Y-%m-%d') - except KeyError: - time_diff = 0 - early_warning_date = end_time - continue - - values = values + [time_diff,early_warning_date,stop_update_period] - last_update_times_df.loc[len(last_update_times_df)] = values - - logger.info(f"Column {column} was last updated at {last_update_time}") - y_last_update_time = last_update_times_df[last_update_times_df['feature']=='y']['warning_date'].values[0] - last_update_times_df.to_csv(os.path.join(dataset,'last_update_times.csv'), index=False) - logger.info('特征停更信息保存到文件：last_update_times.csv') - return last_update_times_df,y_last_update_time - - - -# 统计特征频度 -def featurePindu(dataset): - # 读取文件 - df = loadcsv(os.path.join(dataset,'未填充的特征数据.csv')) - df['ds'] = pd.to_datetime(df['ds']) - # 按ds正序排序，重置索引 - df = df.sort_values(by='ds', ascending=True).reset_index(drop=True) - - # 统计特征频度 - # 每列随机抽取10个值，计算出5个时间间隔，统计每个时间间隔的频度 - columns = df.columns.to_list() - columns.remove('ds') - count_dict = {} - for column in columns: - # 获取每列时间间隔 - values = df[[column,'ds']] - values.dropna(inplace=True,axis=0) - values=values.reset_index(drop=True) - - # 抽取20%个值 - value = values.sample(frac=0.2) - index = value.index - next_index = index + 1 - count = [] - for i,j in zip(index, next_index): - #通过索引计算日期差 - try: - count.append((values.loc[j,'ds'] - values.loc[i,'ds']).days) - except: - pass - # 把31 换成 30 - count = [30 if i == 31 else i for i in count] - # 保留count中出现次数最多的数 - try: - count = max(set(count), key=count.count) - except ValueError : - logger.info(f'{column}列数据为空') - continue - # 存储到字典中 - count_dict[column] = count - - df = pd.DataFrame(count_dict,index=['count']).T - pindu_dfs = pd.DataFrame() - # 根据count分组 - # 输出特征频度统计 - pindudict = {'1':'日度','3':'日度','7':'周度','30':'月度','90':'季度','180':'半年度','365':'年度'} - for i in df.groupby('count'): - # 获取 i[1] 的索引值 - index = i[1].index - pindu_df = pd.DataFrame() - try: - pindu_df[pindudict[str(i[0])]+f'({len(i[1])})'] = index - except KeyError : - pindu_df[str(i[0])+f'天({len(i[1])})'] = index - # 合并到pindu_dfs - pindu_dfs = pd.concat([pindu_dfs,pindu_df],axis=1) - # nan替换为 ' ' - pindu_dfs = pindu_dfs.fillna('') - pindu_dfs.to_csv(os.path.join(dataset,'特征频度统计.csv'),index=False) - logger.info(pindu_dfs) - featureInfo = f'特征信息：总共有{len(columns)-2}个' - for i in pindu_dfs.columns: - featureInfo += f',{i}' - - featureInfo += ', 详看 附1、特征列表' - - featureInfo += ''' - 数据特征工程： - 1. 数据日期排序，新日期在最后 - 2. 删除空列，特征数据列没有值，就删除 - 3. 删除近两月不再更新值的指标 - 4. 非日度数据填充为日度数据，填充规则： - -- 向后填充，举例：假设周五出现一个周度指标数据，那么在这之前的数据用上周五的数据 - -- 向前填充，举例：采集数据开始日期为2018年1月1日，那么周度数据可能是2018年1月3日，那么3日的数据向前填充，使1日2日都有数值 - 数据特征相关性分析： - ''' - logger.info(featureInfo) - with open(os.path.join(dataset,'特征频度统计.txt'), 'w', encoding='utf-8') as f: - f.write(featureInfo) - logger.info('*'*200) - - -def featureAnalysis(df,dataset,y): - # 特征筛选 - import matplotlib.pyplot as plt - # 选择特征和标签列 - X = df.drop(['ds', 'y'], axis=1) # 特征集，排除时间戳和标签列 - yy = df['y'] # 标签集 - - # 标签集自相关函数分析 - from statsmodels.graphics.tsaplots import plot_acf - plot_acf(yy, lags=30) - plt.savefig(os.path.join(dataset,'指标数据自相关图.png')) - plt.close() - - # 标签集偏自相关函数分析 - from statsmodels.graphics.tsaplots import plot_pacf - plot_pacf(yy, lags=30) - plt.savefig(os.path.join(dataset,'指标数据偏自相关图.png')) - plt.close() - - # 画 特征与价格散点图 - # 删除所有*散点图.png - for file in os.listdir(dataset): - if file.endswith("散点图.png"): - os.remove(os.path.join(dataset, file)) - plt.rcParams['font.sans-serif'] = ['SimHei'] - plt.rcParams['axes.unicode_minus'] = False - plt.figure(figsize=(10, 10)) - # # 遍历X每一列，和yy画散点图 ， - # for i, col in enumerate(X.columns): - # plt.subplot(2, 2, i%4+1) - # plt.scatter(X[col], yy) - # plt.xlabel(col) - # plt.ylabel(y) - # plt.title(col) - # if i % 4 == 3 or i == len(X.columns)-1: - # plt.tight_layout() - # plt.savefig(os.path.join(dataset,f'{i}指标数据特征与价格散点图.png')) - # plt.close() - - - -def corr_feature(df): - # 重新命名列名，列名排序,y在第一个 - df.reindex(['y'] + sorted(df.columns.difference(['y']))) - df_test = df.copy() - # 取最后的220行 - df_test = df_test.tail(220) - # 去掉日期列 - df_test = df_test.drop(columns=['ds']) - # 不参与标准化 - df_test_noscaler = df_test.copy() # 滞后处理备份 - df_noscaler = df_test.copy() - # 画出相关性热力图 - df_test.to_csv(os.path.join(dataset,'同步相关性.csv')) - corr = df_test.corr() - # 保存相关系数 - corr.to_csv(os.path.join(dataset,'同步相关性系数.csv')) - # plt.figure(figsize=(10, 10)) - # sns.heatmap(corr, annot=True, cmap='coolwarm') - # plt.savefig('dataset/同步相关性热力图.png') - # plt.show() - - # 读取滞后周期文件，更改特征 - characteristic_period = pd.read_csv('dataset/特征滞后周期.csv',encoding='utf-8') - # 去掉周期为0的行 - characteristic_period = characteristic_period.drop(characteristic_period[characteristic_period['滞后周期'] == 0].index) - for col in df.columns: - # 跳过y列 - if col in ['y']: - continue - # 特征滞后n个周期，计算与y的相关性 - if col in characteristic_period['特征'].values: - # 获取特征对应的周期 - period = characteristic_period[characteristic_period['特征'] == col]['滞后周期'].values[0] - # 滞后处理 - df[col] = df[col].shift(period) - df.to_csv(os.path.join(dataset,'滞后处理后的数据集.csv')) - - - # corr_feture_noscaler = {} # 保存相关性最大的周期 - # 遍历df_test的每一列，计算相关性 - # for col in df_noscaler.columns: - # # 跳过y列 - # if col in ['y']: - # continue - # logger.info('特征：', col) - # # 特征滞后n个周期，计算与y的相关性 - # corr_dict = {} - # try: - # for i in range(0, 200): - # if i == 0: - # df_noscaler[col+'_'+str(i)] = df_noscaler[col] - # else: - # df_noscaler[col+'_'+str(i)] = df_noscaler[col].shift(i) - # corr_dict[col+'_'+str(i)] = abs(df_noscaler[col+'_'+str(i)].corr(df_noscaler['y'])) - # except : - # logger.info('特征：', col, '滑动错误，请查看') - # continue - # 输出相关性最大的特征 - # logger.info(max(corr_dict, key=corr_dict.get), corr_dict[max(corr_dict, key=corr_dict.get)]) - # corr_feture_noscaler[col] = max(corr_dict, key=corr_dict.get).split('_')[-1] - # 画出最相关性最大的特征和y的折线图 - # plt.figure(figsize=(10, 5)) - # plt.plot(df_noscaler[max(corr_dict, key=corr_dict.get)], label=max(corr_dict, key=corr_dict.get)) - # # 设置双坐标轴 - # ax1 = plt.gca() - # ax2 = ax1.twinx() - # ax2.plot(df_noscaler['y'], color='r', label='y') - # plt.legend() - # try: - # plt.savefig('dataset/特征与y的折线图_'+max(corr_dict, key=corr_dict.get)+'.png') - # except : - # # ：替换成_ - # plt.savefig('dataset/特征与y的折线图_'+max(corr_dict, key=corr_dict.get).replace(':','_').replace('/','_').replace('(','_').replace(')','_')+'.png') - # plt.close() - # 结果保存到txt文件 - # logger.info('不参与标准化的特征滞后相关性写入txt文件') - # with open('dataset/不参与标准化的特征滞后相关性.txt', 'w') as f: - # for key, value in corr_feture_noscaler.items(): - # f.write('%s:%s\n' % (key, value)) - # 遍历corr_feture_noscaler,更改df - # colnames_noscaler = [] - # for col in corr_feture_noscaler: - # colname = col+'_'+corr_feture_noscaler[col] - # if int(corr_feture_noscaler[col]) == 0: - # continue - # df_test_noscaler[colname] = df_test_noscaler[col].shift(int(corr_feture_noscaler[col])) - # df_test_noscaler = df_test_noscaler.drop(columns=[col]) - # colnames_noscaler.append(colname) - # 去除有空值的行 - # df_test_noscaler = df_test_noscaler.dropna() - # df_test_noscaler.reindex(['y'] + sorted(df_test_noscaler.columns.difference(['y']))) - # df_test_noscaler.to_csv('dataset/不参与标准化的特征滞后相关性.csv', index=False) - # 画出相关性热力图 - # corr = df_test_noscaler.corr() - # 保存相关系数 - # corr.to_csv(os.path.join(dataset,'不参与标准化的特征滞后相关性系数.csv')) - # plt.figure(figsize=(10, 10)) - # sns.heatmap(corr, annot=True, cmap='coolwarm') - # plt.savefig('dataset/不参与标准化的特征滞后相关性热力图.png') - # plt.close() - # # 标准化每列 - # from sklearn.preprocessing import StandardScaler - # scaler = StandardScaler() - # df_test = pd.DataFrame(scaler.fit_transform(df_test), columns=df_test.columns) - # corr_feture = {} # 保存相关性最大的周期 - # # 遍历df_test的每一列，计算相关性 - # for col in df_test.columns: - # # 跳过y列 - # if col == 'y': - # continue - # logger.info('特征：', col) - # # 特征滞后n个周期，计算与y的相关性 - # corr_dict = {} - # try: - # for i in range(0, 200): - # if i == 0: - # df_test[col+'_'+str(i)] = df_test[col] - # else: - # df_test[col+'_'+str(i)] = df_test[col].shift(i) - # corr_dict[col+'_'+str(i)] = abs(df_test[col+'_'+str(i)].corr(df_test['y'])) - # except : - # logger.info('特征：', col, '滑动错误，请查看') - # continue - # # 输出相关性最大的特征 - # logger.info(max(corr_dict, key=corr_dict.get), corr_dict[max(corr_dict, key=corr_dict.get)]) - # corr_feture[col] = max(corr_dict, key=corr_dict.get).split('_')[-1] - - - # # 结果保存到txt文件 - # with open('dataset/标准化的特征滞后相关性.txt', 'w') as f: - # for key, value in corr_feture.items(): - # f.write('%s:%s\n' % (key, value)) - # # 遍历corr_feture,更改df - # colnames = [] - # for col in corr_feture: - # colname = col+'_'+corr_feture[col] - # if int(corr_feture[col]) == 0: - # continue - # df[colname] = df[col].shift(int(corr_feture[col])) - # df = df.drop(columns=[col]) - # colnames.append(colname) - # # 去除有空值的行 - # df = df.dropna() - # df.reindex(['y'] + sorted(df.columns.difference(['y']))) - # df.to_csv('dataset/标准化后的特征滞后相关性.csv', index=False) - # # 画出相关性热力图 - # ds = df['ds'] - # df = df.drop(columns=['ds']) - # corr = df.corr() - # # 保存相关系数 - # corr.to_csv(os.path.join(dataset,'标准化后的特征滞后相关性系数.csv')) - # plt.figure(figsize=(10, 10)) - # sns.heatmap(corr, annot=True, cmap='coolwarm') - # plt.savefig('dataset/标准化后的特征滞后相关性热力图.png') - # plt.show() - # df['ds'] = ds - - # 去除nan值 - df = df.dropna() - return df - - -def calculate_kdj(data, n=9): - ''' - 给传进来的df 添加列： 波动率，最高，最低，k ,d ，j - ''' - data = data.sort_values(by='ds', ascending=True) - # 因为没有高开低价格，利用每日波动率模拟当天最高价和最低价 - data['pctchange'] = data['y'].pct_change() - # 收益为0的用0.01 - data['pctchange'] = data['pctchange'].replace(0,0.01) - data.dropna(inplace=True) - # 重置索引 - data.reset_index(drop=True,inplace=True) - data['high'] = data['y']* (1+abs(data['pctchange'])/2) - data['low'] = data['y']* (1-abs(data['pctchange'])/2) - low_list = data['y'].rolling(window=n, min_periods=1).min() - high_list = data['y'].rolling(window=n, min_periods=1).max() - rsv = ((data['y'] - low_list) / (high_list - low_list)) * 100 - k = pd.Series(50, index=data.index) - d = pd.Series(50, index=data.index) - for i in range(1, len(data)): - k[i] = (2/3 * k[i - 1]) + (1/3 * rsv[i]) - d[i] = (2/3 * d[i - 1]) + (1/3 * k[i]) - j = 3 * k - 2 * d - - data['K'] = k - data['D'] = d - data['J'] = j - # 将包含 KDJ 指标的数据保存到新的 CSV 文件 - data.to_csv('dataset\stock_data_with_kdj.csv', index=False) - # data = data.dropna() - return data - -def check_column(df,col_name,two_months_ago): - ''' - 检查列是否需要删除。 - 该函数会检查列是否为空值列、180天没有更新的列或常数值列。 - 参数: - col_name (str): 列名。 - df (DataFrame): 包含列的 DataFrame。 - 返回: - bool: 如果列需要删除，返回 True；否则，返回 False。 - ''' - if 'ds' in col_name or 'y' in col_name: - return False - df_check_column = df[['ds',col_name,'y']] - df_check_column = df_check_column.dropna() - - if len(df_check_column) == 0: - print(f'空值列：{col_name}') - return True - # 判断是不是常数列 - if df_check_column[(df_check_column['ds']>= two_months_ago)].groupby(col_name).ngroups < 2: - print(f'180没有更新：{col_name}') - return True - - # 判断相关系数大于0.6 - if is_del_corr > 0: - if abs(df_check_column[col_name].corr(df_check_column['y'])) < is_del_corr: - print(f'相关系数小于0.6：{col_name}') - return True - - corresponding_date = df_check_column.iloc[-1]['ds'] - return corresponding_date < two_months_ago - -def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y',dataset='dataset',delweekenday=False,add_kdj=False,is_timefurture=False): - ''' - 原油特征数据处理函数， - 接收的是两个df，一个是指标数据，一个是指标列表 - 输出的是一个df，包含ds，y，指标列 - ''' - df = df_zhibiaoshuju.copy() - - if end_time == '': - end_time = datetime.datetime.now().strftime('%Y-%m-%d') - # 重命名时间列，预测列 - df.rename(columns={datecol:'ds'},inplace=True) - df.rename(columns={y:'y'},inplace=True) - # 按时间顺序排列 - df.sort_values(by='ds',inplace=True) - df['ds'] = pd.to_datetime(df['ds']) - # 获取start_year年到end_time的数据 - df = df[df['ds'].dt.year >= start_year] - df = df[df['ds'] <= end_time] - # last_update_times_df,y_last_update_time = create_feature_last_update_time(df) - # logger.info(f'删除预警的特征前数据量：{df.shape}') - # columns_to_drop = last_update_times_df[last_update_times_df['warning_date'] < y_last_update_time ]['feature'].values.tolist() - # df = df.drop(columns = columns_to_drop) - # logger.info(f'删除预警的特征后数据量：{df.shape}') - # if is_update_warning_data: - # upload_warning_info(last_update_times_df,y_last_update_time) - # 去掉近最后数据对应的日期在六月以前的列，删除近2月的数据是常熟的列 - if is_del_tow_month: - current_date = datetime.datetime.now() - two_months_ago = current_date - timedelta(days=180) - logger.info(f'删除两月不更新特征前数据量：{df.shape}') - columns_to_drop = [] - for clo in df.columns: - if check_column(df,clo,two_months_ago): - columns_to_drop.append(clo) - df = df.drop(columns=columns_to_drop) - - logger.info(f'删除两月不更新特征后数据量：{df.shape}') - - if freq == 'W': - # 按周取样 - df = df.resample('W', on='ds').mean().reset_index() - elif freq == 'M': - # 按月取样 - df = df.resample('M', on='ds').mean().reset_index() - # 删除预测列空值的行 - df = df.dropna(subset=['y']) - logger.info(f'删除预测列为空值的行后数据量：{df.shape}') - df = df.dropna(axis=1, how='all') - logger.info(f'删除全为空值的列后数据量：{df.shape}') - df.to_csv(os.path.join(dataset,'未填充的特征数据.csv'),index=False) - # 去掉指标列表中的columns_to_drop的行 - df_zhibiaoliebiao = df_zhibiaoliebiao[df_zhibiaoliebiao['指标名称'].isin(df.columns.tolist())] - df_zhibiaoliebiao.to_csv(os.path.join(dataset,'特征处理后的指标名称及分类.csv'),index=False) - # 数据频度分析 - featurePindu(dataset=dataset) - # 向上填充 - df = df.ffill() - # 向下填充 - df = df.bfill() - - # 删除周六日的数据 - if delweekenday: - df = df[df['ds'].dt.weekday < 5] - - # kdj指标 - if add_kdj: - df = calculate_kdj(df) - # 衍生时间特征 - if is_timefurture: - df = addtimecharacteristics(df=df,dataset=dataset) - # 特征分析 - featureAnalysis(df,dataset=dataset,y=y) - return df - -def datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y',dataset='dataset',delweekenday=False,add_kdj=False,is_timefurture=False): - ''' - 聚烯烃特征数据处理函数， - 接收的是两个df，一个是指标数据，一个是指标列表 - 输出的是一个df，包含ds，y，指标列 - ''' - df = df_zhibiaoshuju.copy() - if end_time == '': - end_time = datetime.datetime.now().strftime('%Y-%m-%d') - # date转为pddate - df.rename(columns={datecol:'ds'},inplace=True) - - # 指定列统一减少数值 - df[offsite_col] = df[offsite_col]-offsite - # 预测列为avg_cols的均值 - df[y] = df[avg_cols].mean(axis=1) - # 去掉多余的列avg_cols - df = df.drop(columns=avg_cols) - - # 重命名预测列 - df.rename(columns={y:'y'},inplace=True) - # 按时间顺序排列 - df.sort_values(by='ds',inplace=True) - df['ds'] = pd.to_datetime(df['ds']) - # 获取2018年到当前日期的数据 - df = df[df['ds'].dt.year >= 2018] - # 获取小于等于当前日期的数据 - df = df[df['ds'] <= end_time] - logger.info(f'删除两月不更新特征前数据量：{df.shape}') - # 去掉近最后数据对应的日期在两月以前的列，删除近2月的数据是常数的列 - current_date = datetime.datetime.now() - two_months_ago = current_date - timedelta(days=40) - # 检查两月不更新的特征 - def check_column(col_name): - if 'ds' in col_name or 'y' in col_name: - return False - df_check_column = df[['ds',col_name]] - df_check_column = df_check_column.dropna() - if len(df_check_column) == 0: - return True - if df_check_column[(df_check_column['ds']>= two_months_ago)].groupby(col_name).ngroups < 2: - return True - corresponding_date = df_check_column.iloc[-1]['ds'] - return corresponding_date < two_months_ago - columns_to_drop = df.columns[df.columns.map(check_column)].tolist() - df = df.drop(columns = columns_to_drop) - - logger.info(f'删除两月不更新特征后数据量：{df.shape}') - - # 删除预测列空值的行 - df = df.dropna(subset=['y']) - logger.info(f'删除预测列为空值的行后数据量：{df.shape}') - df = df.dropna(axis=1, how='all') - logger.info(f'删除全为空值的列后数据量：{df.shape}') - df.to_csv(os.path.join(dataset,'未填充的特征数据.csv'),index=False) - # 去掉指标列表中的columns_to_drop的行 - df_zhibiaoliebiao = df_zhibiaoliebiao[df_zhibiaoliebiao['指标名称'].isin(df.columns.tolist())] - df_zhibiaoliebiao.to_csv(os.path.join(dataset,'特征处理后的指标名称及分类.csv'),index=False) - # 频度分析 - featurePindu(dataset=dataset) - # 向上填充 - df = df.ffill() - # 向下填充 - df = df.bfill() - - # 删除周六日的数据 - if delweekenday: - df = df[df['ds'].dt.weekday < 5] - - if add_kdj: - df = calculate_kdj(df) - - if is_timefurture: - df = addtimecharacteristics(df=df,dataset=dataset) - - featureAnalysis(df,dataset=dataset,y=y) - return df - -def getdata(filename, datecol='date',y='y',dataset='',add_kdj=False,is_timefurture=False,end_time=''): - logger.info('getdata接收：'+filename+' '+datecol+' '+end_time) - # 判断后缀名 csv或excel - if filename.endswith('.csv'): - df = loadcsv(filename) - else: - # 读取excel 指标数据 - df_zhibiaoshuju = pd.read_excel(filename,sheet_name='指标数据') - df_zhibiaoliebiao = pd.read_excel(filename,sheet_name='指标列表') - - - # 日期字符串转为datatime - df = datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol,y = y,dataset=dataset,add_kdj=add_kdj,is_timefurture=is_timefurture,end_time=end_time) - - return df,df_zhibiaoliebiao -def getdata_juxiting(filename, datecol='date',y='y',dataset='',add_kdj=False,is_timefurture=False,end_time=''): - logger.info('getdata接收：'+filename+' '+datecol+' '+end_time) - # 判断后缀名 csv或excel - if filename.endswith('.csv'): - df = loadcsv(filename) - else: - # 读取excel 指标数据 - df_zhibiaoshuju = pd.read_excel(filename,sheet_name='指标数据') - df_zhibiaoliebiao = pd.read_excel(filename,sheet_name='指标列表') - - # 日期字符串转为datatime - df = datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,datecol,y = y,dataset=dataset,add_kdj=add_kdj,is_timefurture=is_timefurture,end_time=end_time) - - return df - - -def sanitize_filename(filename): - # 使用正则表达式替换不合规的字符 - # 这里我们替换为下划线'_'，但你可以根据需要选择其他字符 - sanitized = re.sub(r'[\\/*?:"<>|\s]', '_', filename) - # 移除开头的点（在某些系统中，以点开头的文件可能是隐藏的） - sanitized = re.sub(r'^\.', '', sanitized) - # 如果需要，可以添加更多替换规则 - return sanitized - -class BinanceAPI: - ''' - 获取 Binance API 请求头签名 - ''' - def __init__(self, APPID, SECRET): - self.APPID = APPID - self.SECRET = SECRET - self.get_signature() - - # 生成随机字符串作为 nonce - def generate_nonce(self, length=32): - self.nonce = ''.join(random.choices(string.ascii_letters + string.digits, k=length)) - return self.nonce - - # 获取当前时间戳（秒） - def get_timestamp(self): - return int(time.time()) - - # 构建待签名字符串 - def build_sign_str(self): - return f'appid={self.APPID}&nonce={self.nonce}×tamp={self.timestamp}' - - # 使用 HMAC SHA-256 计算签名 - def calculate_signature(self, secret, message): - return base64.urlsafe_b64encode(hmac.new(secret.encode('utf-8'), message.encode('utf-8'), hashlib.sha256).digest()).decode('utf-8') - - def get_signature(self): - # 调用上述方法生成签名 - self.nonce = self.generate_nonce() - self.timestamp = self.get_timestamp() - self.sign_str = self.build_sign_str() - self.signature = self.calculate_signature(self.SECRET, self.sign_str) - # return self.signature - -class Graphs: - # 绘制标题 - @staticmethod - def draw_title(title: str): - # 获取所有样式表 - style = getSampleStyleSheet() - # 拿到标题样式 - ct = style['Heading1'] - # 单独设置样式相关属性 - ct.fontName = 'SimSun' # 字体名 - ct.fontSize = 18 # 字体大小 - ct.leading = 50 # 行间距 - ct.textColor = colors.green # 字体颜色 - ct.alignment = 1 # 居中 - ct.bold = True - # 创建标题对应的段落，并且返回 - return Paragraph(title, ct) - - # 绘制小标题 - @staticmethod - def draw_little_title(title: str): - # 获取所有样式表 - style = getSampleStyleSheet() - # 拿到标题样式 - ct = style['Normal'] - # 单独设置样式相关属性 - ct.fontName = 'SimSun' # 字体名 - ct.fontSize = 15 # 字体大小 - ct.leading = 30 # 行间距 - ct.textColor = colors.red # 字体颜色 - # 创建标题对应的段落，并且返回 - return Paragraph(title, ct) - - # 绘制普通段落内容 - @staticmethod - def draw_text(text: str): - # 获取所有样式表 - style = getSampleStyleSheet() - # 获取普通样式 - ct = style['Normal'] - ct.fontName = 'SimSun' - ct.fontSize = 12 - ct.wordWrap = 'CJK' # 设置自动换行 - ct.alignment = 0 # 左对齐 - ct.firstLineIndent = 32 # 第一行开头空格 - ct.leading = 25 - return Paragraph(text, ct) - - # 绘制表格 - @staticmethod - def draw_table(*args): - # 列宽度 - col_width = args[0] - style = [ - ('FONTNAME', (0, 0), (-1, -1), 'SimSun'), # 字体 - ('FONTSIZE', (0, 0), (-1, 0), 12), # 第一行的字体大小 - ('FONTSIZE', (0, 1), (-1, -1), 10), # 第二行到最后一行的字体大小 - ('BACKGROUND', (0, 0), (-1, 0), '#d5dae6'), # 设置第一行背景颜色 - ('ALIGN', (0, 0), (-1, -1), 'CENTER'), # 第一行水平居中 - ('ALIGN', (0, 1), (-1, -1), 'LEFT'), # 第二行到最后一行左右左对齐 - ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'), # 所有表格上下居中对齐 - ('TEXTCOLOR', (0, 0), (-1, -1), colors.darkslategray), # 设置表格内文字颜色 - ('GRID', (0, 0), (-1, -1), 0.5, colors.grey), # 设置表格框线为grey色，线宽为0.5 - # ('SPAN', (0, 1), (0, 2)), # 合并第一列二三行 - # ('SPAN', (0, 3), (0, 4)), # 合并第一列三四行 - # ('SPAN', (0, 5), (0, 6)), # 合并第一列五六行 - # ('SPAN', (0, 7), (0, 8)), # 合并第一列五六行 - ] - table = Table(args[1:], colWidths=col_width, style=style) - return table - - # 创建图表 - @staticmethod - def draw_bar(bar_data: list, ax: list, items: list): - drawing = Drawing(500, 250) - bc = VerticalBarChart() - bc.x = 45 # 整个图表的x坐标 - bc.y = 45 # 整个图表的y坐标 - bc.height = 200 # 图表的高度 - bc.width = 350 # 图表的宽度 - bc.data = bar_data - bc.strokeColor = colors.black # 顶部和右边轴线的颜色 - bc.valueAxis.valueMin = 5000 # 设置y坐标的最小值 - bc.valueAxis.valueMax = 26000 # 设置y坐标的最大值 - bc.valueAxis.valueStep = 2000 # 设置y坐标的步长 - bc.categoryAxis.labels.dx = 2 - bc.categoryAxis.labels.dy = -8 - bc.categoryAxis.labels.angle = 20 - bc.categoryAxis.categoryNames = ax - - # 图示 - leg = Legend() - leg.fontName = 'SimSun' - leg.alignment = 'right' - leg.boxAnchor = 'ne' - leg.x = 475 # 图例的x坐标 - leg.y = 240 - leg.dxTextSpace = 10 - leg.columnMaximum = 3 - leg.colorNamePairs = items - drawing.add(leg) - drawing.add(bc) - return drawing - - # 绘制图片 - @staticmethod - def draw_img(path): - img = Image(path) # 读取指定路径下的图片 - img.drawWidth = 20*cm # 设置图片的宽度 - img.drawHeight = 10*cm # 设置图片的高度 - return img - -# 定义样式函数 -def style_row(row): - if '周' in row['频度']: - return ['background-color: yellow'] * len(row) - else: - return ['background-color: gray'] * len(row) - - - -class EtaReader(): - def __init__(self,signature,classifylisturl,classifyidlisturl,edbcodedataurl,edbcodelist,edbdatapushurl,edbdeleteurl,edbbusinessurl): - ''' - 初始化 EtaReader 类的实例。 - - 参数: - signature (str): 用于 API 请求的签名。 - classifylisturl (str): 分类列表的 URL。 - classifyidlisturl (str): 分类 ID 列表的 URL。 - edbcodedataurl (str): EDB 代码数据的 URL。 - edbdatapushurl (str): EDB 数据推送的 URL。 - edbcodelist (str): EDB 代码列表的 URL。 - edbdeleteurl (str): EDB 数据删除的 URL。 - edbbusinessurl (str): EDB 业务数据的 URL。 - - 返回: - None - ''' - self.signature = signature - self.classifylisturl = classifylisturl - self.classifyidlisturl = classifyidlisturl - self.edbcodedataurl = edbcodedataurl - self.edbdatapushurl = edbdatapushurl - self.edbcodelist = edbcodelist - self.edbdeleteurl = edbdeleteurl - self.edbbusinessurl = edbbusinessurl - - - def filter_yuanyou_data(self,ClassifyName,data): - ''' - 指标名称保留规则 - ''' - - # 包含 关键词 去除， 返回flase - if any(keyword in data for keyword in ['运费','检修','波动率','地缘政治','股价', - '同比','环比','环差','裂差','4WMA','变频','道琼斯','标普500','纳斯达克', - '四周均值','名占比','残差','DMA', - '连7-连9','4周平均','4周均值','滚动相关性','日本']): - return False - - # 检查需要的特征 - # 去掉 分析 分类下的数据 - if ClassifyName == '分析': - return False - - # 保留 库存中特殊关键词 - if ClassifyName == '库存': - if any(keyword in data for keyword in ['原油' , '美国' ,'全球' ,'中国' ,'富查伊拉','ARA' ]): - return True - else: - pass - else: - pass - - # 去掉 持仓中不是基金的数据 - if ClassifyName == '持仓': - if '基金' not in data: - return False - else: - pass - else: - pass - - # 去掉 航班中不是中国、美国 的数据 - if ClassifyName == '需求': - if '航班' in data : - if '中国' in data or '美国' in data : - return True - else: - return False - else: - pass - else: - pass - - # 分类为 期货市场，同质性数据取第一个 - if ClassifyName == '期货市场': - # 去掉c1-9 以后的 - if 'c1-c' in data: - try: - c = int(data.split('c1-c')[1]) - except: - return False - if c > 9 : - return False - else: - pass - - else: - pass - - # 判断 同质性数据， 字符串开头 - strstartdict = {'ICE Brent c':"ICE Brent c14", - 'NYMWX WTI c':"NYMWX WTI c5", - 'INE SC c':"INE SC c1", - 'EFS c':"EFS c", - 'Dubai Swap c':"Dubai Swap c1", - 'Oman Swap c':"Oman Swap c1", - 'DME Oman c':"DME Oman c1", - 'Murban Futures c':"Murban Futures c1", - 'Dubai连合约价格':'Dubai连1合约价格', - '美国RBOB期货月份合约价格':'美国RBOB期货2309月份合约价格', - 'Brent连合约价格':'Brent连1合约价格', - 'WTI连合约价格':'WTI连1合约价格', - '布伦特连合约价格':'Brent连1合约价格', - 'Brent 连合约价格':'Brent连1合约价格', - 'Dubai连合约价格':'Dubai连1合约价格', - 'Brent连':'Brent连1合约价格', - 'brent连':'Brent连1合约价格', - } - # 判断名称字符串开头是否在 strstartdict.keys中 - match = re.match(r'([a-zA-Z\s]+)(\d+)', data) - if match: - part1 = match.group(1) - part2 = match.group(2) - if part1 in [i for i in strstartdict.keys()]: - if data == strstartdict[part1]: - return True - else: - return False - # data = 'Brent 连7合约价格' - # 判断名称字符串去掉数字后是否在 strstartdict.keys中 - match = re.findall(r'\D+', data) - if match : - if len(match) == 2: - part1 = match[0] - part2 = match[1] - if part1+part2 in [i for i in strstartdict.keys()]: - if data == strstartdict[part1+part2]: - return True - else: - return False - else: - pass - elif len(match) == 1: - match = re.findall(r'\D+', data) - part1 = match[0] - - if part1 in [i for i in strstartdict.keys()]: - if data == strstartdict[part1]: - return True - else: - return False - else: - pass - else: - pass - - # 去掉kpler数据源 - if 'Kpler' in ClassifyName or 'kpler' in ClassifyName: - return False - - return True - - def filter_pp_data(self,ClassifyName,data): - ''' - 指标名称保留规则 - ''' - - # 包含 关键词 去除， 返回flase - # if any(keyword in data for keyword in ['运费','检修','波动率','地缘政治','股价', - # '同比','环比','环差','裂差','4WMA','变频','道琼斯','标普500','纳斯达克', - # '四周均值','名占比','残差','DMA', - # '连7-连9','4周平均','4周均值','滚动相关性','日本']): - # return False - # 包含 关键词 保留， 返回True - if any(keyword in data for keyword in ['拉丝']): - return True - - - - # 检查需要的特征 - # 去掉 期货市场 分类下的数据 - if ClassifyName == '期货市场': - return False - else: - pass - - # 保留 库存 下所有指标 - if ClassifyName == '库存': - return True - else: - pass - - # 保留 进出口 下所有指标 - if ClassifyName == '进出口': - return True - else: - pass - - # 保留 价差 下所有指标 - if ClassifyName == '价差': - return True - else: - pass - - # 保留 供应 下所有指标 - if ClassifyName == '供应': - return True - else: - pass - - - # 保留 需求 下所有指标 - if ClassifyName == '需求': - return True - else: - pass - - - return True - - # 通过edbcode 获取指标数据 - def edbcodegetdata(self,df,EdbCode,EdbName): - # 根据指标id，获取指标数据 - url = self.edbcodedataurl+str(EdbCode) - # 发送GET请求 - response = requests.get(url, headers=self.headers) - - # 检查响应状态码 - if response.status_code == 200: - data = response.json() # 假设接口返回的是JSON数据 - all_data_items = data.get('Data') - # 列表转换为DataFrame - df3 = pd.DataFrame(all_data_items, columns=['DataTime', 'Value', 'UpdateTime']) - # df3 = pd.read_json(all_data_items, orient='records') - - # 去掉UpdateTime 列 - df3 = df3.drop(columns=['UpdateTime']) - # df3.set_index('DataTime') - df3.rename(columns={'Value': EdbName}, inplace=True) - # 将数据存储df1 - df = pd.merge(df, df3, how='outer',on='DataTime',suffixes= ('', '_y')) - # 按时间排序 - df = df.sort_values(by='DataTime', ascending=True) - return df - - else: - # 请求失败，打印错误信息 - logger.info(f'Error: {response.status_code}, {response.text}') - # 主动抛出异常 - raise Exception(f'Error: {response.status_code}, {response.text}') - - def get_eta_api_yuanyou_data(self,data_set,dataset=''): - ''' - 从ETA API获取原油数据 - - 参数: - data_set (str): 数据集名称 - dataset (str): 数据集ID，默认为空 - - 返回: - None - ''' - today = datetime.date.today().strftime("%Y-%m-%d") - - # 定义你的headers，这里可以包含多个参数 - self.headers = { - 'nonce': self.signature.nonce, # 例如，一个认证令牌 - 'timestamp': str(self.signature.timestamp), # 自定义的header参数 - 'appid': self.signature.APPID, # 另一个自定义的header参数 - 'signature': self.signature.signature - } - - # 从列表数据中获取指标名称，判断指标名称频度是否为日 ，如果是，则获取UniqueCode，然后获取指标数据，保存到xlat文件中的sheet表。 - - ''' - df = sheetname 指标列表，存储 指标分类-指标名称-指标id-频度 - df1 = sheetname 指标数据 ,存储 时间-指标名称1-指标名称2... - - ''' - - # 构建新的DataFrame df df1 - df = pd.DataFrame(columns=['指标分类', '指标名称', '指标id', '频度','指标来源','来源id','最后更新时间','更新周期','预警日期','停更周期']) - df1 = pd.DataFrame(columns=['DataTime']) - - - # 外网环境无法访问，请确认是否为内网环境 - try: - # 发送GET请求 获取指标分类列表 - response = requests.get(self.classifylisturl, headers=self.headers) - except requests.exceptions.RequestException as e: - raise Exception(f"请求失败,请确认是否为内网环境: {e}","\033[0m") - - # 检查响应状态码 - if response.status_code == 200: - # 获取成功， 处理响应内容 - data = response.json() # 假设接口返回的是JSON数据 - - # 请求成功，处理响应内容 - # logger.info(data.get('Data')) - # 定义你想要保留的固定值 - fixed_value = 1214 - - # 遍历列表，只保留那些'category' key的值为固定值的数据项 - filtered_data = [item for item in data.get('Data') if item.get('ParentId') == fixed_value] - - #然后循环filtered_data去获取list数据，才能获取到想要获取的ClassifyId - n = 0 - for item in filtered_data: - n+= 1 - # if n>50: - # break - ClassifyId = item["ClassifyId"] #分类id，分类下的指标列表接口的请求参数 - ClassifyName = item["ClassifyName"] #分类名称，要保存到df的指标分类列 - # 根据分类id，获取指标列表 - url = self.classifyidlisturl+str(ClassifyId) - response = requests.get(url, headers=self.headers) - if response.status_code == 200: - # logger.info(response.text) - data2 = response.json() - Data = data2.get('Data') - for i in Data: - # s+= 1 - EdbCode = i.get('EdbCode') - EdbName = i.get('EdbName') # 指标名称，要保存到df2的指标名称列,df的指标名称列 - Frequency = i.get('Frequency') # 频度，要保存到df的频度列 - SourceName = i.get('SourceName') # 来源名称，要保存到df的频度列 - Source = i.get('Source') # 来源ID，要保存到df的频度列 - Unit = i.get('Unit') # 单位，要保存到df的单位列 - # 频度不是 日 或者 周的 跳过 - if Frequency not in ['日度','周度','日','周']: - continue - - # 只保留手工数据中，名称带有 海运出口 海运进口 - if Source == 9 and not ('海运出口' in EdbName or '海运进口' in EdbName): - continue - - # 不要wind数据 - if Source == 2: - continue - - - # 判断名称是否需要保存 - isSave = self.filter_yuanyou_data(ClassifyName,EdbName) - if isSave: - # 保存到df - df1 = self.edbcodegetdata(df1,EdbCode,EdbName) - # 取df1所有行最后一列 - edbname_df = df1[['DataTime',f'{EdbName}']] - edbname_df = edbname_df.dropna() - - if len(edbname_df) == 0: - logger.info(f'指标名称：{EdbName} 没有数据') - continue - try: - time_sequence = edbname_df['DataTime'].values.tolist()[-10:] - except IndexError: - time_sequence = edbname_df['DataTime'].values.tolist() - # 使用Counter来统计每个星期几的出现次数 - from collections import Counter - weekday_counter = Counter(datetime.datetime.strptime(time_str, "%Y-%m-%d").strftime('%A') for time_str in time_sequence) - - # 打印出现次数最多的星期几 - try: - most_common_weekday = weekday_counter.most_common(1)[0][0] - # 计算两周后的日期 - warning_date = (datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d") + datetime.timedelta(weeks=2)).strftime("%Y-%m-%d") - stop_update_period = (datetime.datetime.strptime(today, "%Y-%m-%d") - datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d")).days // 7 - - except IndexError: - most_common_weekday = '其他' - stop_update_period = 0 - if '日' in Frequency: - most_common_weekday = '每天' - warning_date = (datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d") + datetime.timedelta(days=3)).strftime("%Y-%m-%d") - stop_update_period = (datetime.datetime.strptime(today, "%Y-%m-%d") - datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d")).days - - # 保存频度 指标名称 分类 指标id 到 df - df2 = pd.DataFrame({'指标分类': ClassifyName, - '指标名称': EdbName, - '指标id': EdbCode, - '单位': Unit, - '频度': Frequency, - '指标来源':SourceName, - '来源id':Source, - '最后更新时间':edbname_df['DataTime'].values[-1], - '更新周期':most_common_weekday, - '预警日期':warning_date, - '停更周期':stop_update_period},index=[0], - ) - - # df = pd.merge(df, df2, how='outer') - df = pd.concat([df, df2]) - else: - logger.info(f'跳过指标 {EdbName}') - - # 找到列表中不在指标列中的指标id，保存成新的list - new_list = [item for item in self.edbcodelist if item not in df['指标id'].tolist()] - logger.info(new_list) - # 遍历new_list，获取指标数据，保存到df1 - for item in new_list: - logger.info(item) - # 将item 加入到 df['指标id']中 - try: - itemname = edbcodenamedict[item] - except: - itemname = item - - df1 = self.edbcodegetdata(df1,item,itemname) - df = pd.concat([df, pd.DataFrame({'指标分类': '其他', '指标名称': itemname, '指标id': item, '频度': '其他','指标来源':'其他','来源id':'其他'},index=[0])]) - - # 按时间排序 - df1.sort_values('DataTime',inplace=True,ascending=False) - df1.rename(columns={'DataTime': 'date'},inplace=True) - # df1.dropna(inplace=True) - # 去掉大于今天日期的行 - df1 = df1[df1['date'] <= datetime.datetime.now().strftime('%Y-%m-%d')] - logger.info(df1.head()) - # logger.info(f'{df1.head()}') - - df_zhibiaoshuju = df1.copy() - df_zhibiaoliebiao = df.copy() - return df_zhibiaoshuju,df_zhibiaoliebiao - - def get_eta_api_pp_data(self,data_set,dataset=''): - global ClassifyId - today = datetime.date.today().strftime("%Y-%m-%d") - - # 定义你的headers，这里可以包含多个参数 - self.headers = { - 'nonce': self.signature.nonce, # 例如，一个认证令牌 - 'timestamp': str(self.signature.timestamp), # 自定义的header参数 - 'appid': self.signature.APPID, # 另一个自定义的header参数 - 'signature': self.signature.signature - } - - # 从列表数据中获取指标名称，判断指标名称频度是否为日 ，如果是，则获取UniqueCode，然后获取指标数据，保存到xlat文件中的sheet表。 - - ''' - df = sheetname 指标列表，存储 指标分类-指标名称-指标id-频度 - df1 = sheetname 指标数据 ,存储 时间-指标名称1-指标名称2... - - ''' - - # 构建新的DataFrame df df1 - df = pd.DataFrame(columns=['指标分类', '指标名称', '指标id', '频度']) - df1 = pd.DataFrame(columns=['DataTime']) - - - # 外网环境无法访问，请确认是否为内网环境 - try: - # 发送GET请求 获取指标分类列表 - response = requests.get(self.classifylisturl, headers=self.headers) - except requests.exceptions.RequestException as e: - raise Exception(f"请求失败,请确认是否为内网环境: {e}","\033[0m") - - # 检查响应状态码 - if response.status_code == 200: - # 获取成功， 处理响应内容 - data = response.json() # 假设接口返回的是JSON数据 - - # 请求成功，处理响应内容 - # logger.info(data.get('Data')) - # 定义你想要保留的固定值 - fixed_value = ClassifyId - - # 遍历列表，只保留那些'category' key的值为固定值的数据项 - filtered_data = [item for item in data.get('Data') if item.get('ParentId') == fixed_value] - - #然后循环filtered_data去获取list数据，才能获取到想要获取的ClassifyId - n = 0 - for item in filtered_data: - n+= 1 - # if n>50: - # break - ClassifyId = item["ClassifyId"] #分类id，分类下的指标列表接口的请求参数 - ClassifyName = item["ClassifyName"] #分类名称，要保存到df的指标分类列 - # 根据分类id，获取指标列表 - url = self.classifyidlisturl+str(ClassifyId) - response = requests.get(url, headers=self.headers) - if response.status_code == 200: - # logger.info(response.text) - data2 = response.json() - Data = data2.get('Data') - for i in Data: - # s+= 1 - EdbCode = i.get('EdbCode') - EdbName = i.get('EdbName') # 指标名称，要保存到df2的指标名称列,df的指标名称列 - Frequency = i.get('Frequency') # 频度，要保存到df的频度列 - # 频度不是 日 或者 周的 跳过 - if Frequency not in ['日度','周度','日','周']: - continue - - # 判断名称是否需要保存 - isSave = self.filter_pp_data(ClassifyName,EdbName) - if isSave: - # 保存到df - # 保存频度 指标名称 分类 指标id 到 df - df2 = pd.DataFrame({'指标分类': ClassifyName, '指标名称': EdbName, '指标id': EdbCode, '频度': Frequency},index=[0]) - - # df = pd.merge(df, df2, how='outer') - df = pd.concat([df, df2]) - df1 = self.edbcodegetdata(df1,EdbCode,EdbName) - else: - logger.info(f'跳过指标 {EdbName}') - - # 找到列表中不在指标列中的指标id，保存成新的list - new_list = [item for item in self.edbcodelist if item not in df['指标id'].tolist()] - logger.info(new_list) - # 遍历new_list，获取指标数据，保存到df1 - for item in new_list: - logger.info(item) - # 将item 加入到 df['指标id']中 - try: - itemname = edbcodenamedict[item] - except: - itemname = item - - df1 = self.edbcodegetdata(df1,item,itemname) - df = pd.concat([df, pd.DataFrame({'指标分类': '其他', '指标名称': itemname, '指标id': item, '频度': '其他'},index=[0])]) - - # 按时间排序 - df1.sort_values('DataTime',inplace=True,ascending=False) - df1.rename(columns={'DataTime': 'date'},inplace=True) - # df1.dropna(inplace=True) - # 去掉大于今天日期的行 - df1 = df1[df1['date'] <= datetime.datetime.now().strftime('%Y-%m-%d')] - logger.info(df1.head()) - # logger.info(f'{df1.head()}') - # 保存到xlsx文件的sheet表 - with pd.ExcelWriter(os.path.join(dataset,data_set)) as file: - df1.to_excel(file, sheet_name='指标数据', index=False) - df.to_excel(file, sheet_name='指标列表', index=False) - - df_zhibiaoshuju = df1.copy() - df_zhibiaoliebiao = df.copy() - return df_zhibiaoshuju,df_zhibiaoliebiao - - def push_data(self,data): - - today = datetime.date.today().strftime("%Y-%m-%d") - - # 定义你的headers，这里可以包含多个参数 - self.headers = { - 'nonce': self.signature.nonce, # 例如，一个认证令牌 - 'timestamp': str(self.signature.timestamp), # 自定义的header参数 - 'appid': self.signature.APPID, # 另一个自定义的header参数 - 'signature': self.signature.signature - } - - # 发送post请求 上传数据 - logger.info('请求参数：',data) - response = requests.post(self.edbdatapushurl, headers=self.headers,data=json.dumps(data)) - - # 检查响应状态码 - if response.status_code == 200: - data = response.json() # 假设接口返回的是JSON数据 - - logger.info('上传成功，响应为：', data) - - else: - # 请求失败，打印错误信息 - logger.info(f'Error: {response.status_code}, {response.text}') - # 主动抛出异常 - raise Exception(f'Error: {response.status_code}, {response.text}') - - def del_zhibiao(self,IndexCodeList): - today = datetime.date.today().strftime("%Y-%m-%d") - - # 定义你的headers，这里可以包含多个参数 - self.headers = { - 'nonce': self.signature.nonce, # 例如，一个认证令牌 - 'timestamp': str(self.signature.timestamp), # 自定义的header参数 - 'appid': self.signature.APPID, # 另一个自定义的header参数 - 'signature': self.signature.signature - } - - data = { - "IndexCodeList": IndexCodeList #指标编码列表 - } - # 发送post请求 上传数据 - response = requests.post(self.edbdeleteurl, headers=self.headers,data=json.dumps(data)) - - - # 检查响应状态码 - if response.status_code == 200: - data = response.json() # 假设接口返回的是JSON数据 - - logger.info('删除成功，响应为：', data) - - else: - # 请求失败，打印错误信息 - logger.info(f'Error: {response.status_code}, {response.text}') - # 主动抛出异常 - raise Exception(f'Error: {response.status_code}, {response.text}') - - def del_business(self,data): - '''' - 接口地址 - https://console-docs.apipost.cn/preview/fce869601d0be1d9/9a637c2f9ed0c589?target_id=d3cafcbf-a68c-42b3-b105-7bbd0e95a9cd - - 请求体 body - { - "IndexCode": "W001067", //指标编码 - "StartDate": "2020-04-20", //指标需要删除的开始日期（>=），如果开始日期和结束日期相等，那么就是删除该日期 - "EndDate": "2024-05-28" //指标需要删除的结束日期（<=），如果开始日期和结束日期相等，那么就是删除该日期 - } - ''' - today = datetime.date.today().strftime("%Y-%m-%d") - - # 定义你的headers，这里可以包含多个参数 - self.headers = { - 'nonce': self.signature.nonce, # 例如，一个认证令牌 - 'timestamp': str(self.signature.timestamp), # 自定义的header参数 - 'appid': self.signature.APPID, # 另一个自定义的header参数 - 'signature': self.signature.signature - } - - - # 发送post请求 上传数据 - response = requests.post(self.edbbusinessurl, headers=self.headers,data=json.dumps(data)) - - - # 检查响应状态码 - if response.status_code == 200: - data = response.json() # 假设接口返回的是JSON数据 - - logger.info('删除成功，响应为：', data) - - else: - # 请求失败，打印错误信息 - logger.info(f'Error: {response.status_code}, {response.text}') - # 主动抛出异常 - raise Exception(f'Error: {response.status_code}, {response.text}') - - -def get_market_data(end_time,df): - """ - 获取市场数据，拼接到df中 - """ - # 获取token - token = get_head_auth_report() - # 定义请求参数 - query_data_list_item_nos_data['data']['dateEnd'] = end_time.replace('-','') - # 发送请求 - headers = {"Authorization": token} - logger.info('获取数据中...') - items_res = requests.post(url=query_data_list_item_nos_url, headers=headers, json=query_data_list_item_nos_data, timeout=(3, 35)) - json_data = json.loads(items_res.text) - logger.info(f"获取到的数据:{json_data}") - df3 = pd.DataFrame(json_data['data']) - # 按照dataItemNo 分组 得到多个dataframe ，最后根据dataDate merge 成一个dataframe - df2 = pd.DataFrame() - for i in df3['dataItemNo'].unique(): - df1 = df3[df3['dataItemNo'] == i] - df1 = df1[['dataDate', 'dataValue']] - df1 = df1.rename(columns={'dataValue': i}) - if len(df2) == 0: - df2 = df1 - continue - df2 = pd.merge(df2, df1, how='left') - df2 = df2.rename(columns={'dataDate': 'date'}) - # 20240101 转换为 2024-01-01 - df2['date'] = pd.to_datetime(df2['date'], format='%Y%m%d') - df2['date'] = df2['date'].dt.strftime('%Y-%m-%d') - df = pd.merge(df, df2, how='left',on='date') - return df - - -def get_high_low_data(df): - # 读取excel 从第五行开始 - df1 = pd.read_excel(os.path.join(dataset,'数据项下载.xls'),header=5, names=['numid','date', 'Brentzdj', 'Brentzgj']) - # 合并数据 - df = pd.merge(df, df1, how='left',on='date') - return df - - -# 时间特征，年，月，一年的多少天，周几，第几周，第几季度，每月的第几天， 每季度的第几天，是否每月的第一天，是否每月的最后一天，是否每季度的第一天，是否每季度的最后一天，是否每年的第一天，是否每年的最后一天 -def addtimecharacteristics(df,dataset): - """ - 为输入的 DataFrame 添加日期相关信息列 - - 参数： - df (pandas.DataFrame): 包含日期列 'ds' 的 DataFrame - - 返回： - pandas.DataFrame: 添加了相关列的 DataFrame - """ - df['year'] = df['ds'].dt.year - df['month'] = df['ds'].dt.month - df['day'] = df['ds'].dt.day - df['dayofweek'] = df['ds'].dt.dayofweek - df['weekofyear'] = df['ds'].dt.isocalendar().week - df['dayofyear'] = df['ds'].dt.dayofyear - df['quarternum'] = df['ds'].dt.quarter - # 将ds列转换为季度Period对象 - df['quarter'] = df['ds'].dt.to_period('Q') - # 获取每个季度的开始日期 - df['quarter_start'] = df['quarter'].dt.to_timestamp('s') - # 计算每个日期是所在季度的第几天 - df['dayofquarter'] = (df['ds'] - df['quarter_start']).dt.days + 1 - # 是否月初 - df['is_month_start'] = df['ds'].dt.is_month_start.astype(int) - # 是否月末 - df['is_month_end'] = df['ds'].dt.is_month_end.astype(int) - # 是否季度初 - df['is_quarter_start'] = df['ds'].dt.is_quarter_start.astype(int) - # 是否季度末 - df['is_quarter_end'] = df['ds'].dt.is_quarter_end.astype(int) - # 是否年初 - df['is_year_start'] = df['ds'].dt.is_year_start.astype(int) - # 是否年末 - df['is_year_end'] = df['ds'].dt.is_year_end.astype(int) - # 去掉 quarter_start quarter - df.drop(columns=['quarter_start','quarter'],inplace=True) - df.to_csv(os.path.join(dataset,'指标数据添加时间特征.csv'), index=False) - return df diff --git a/lib/dataread_yongan.py b/lib/dataread_yongan.py deleted file mode 100644 index 7475f69..0000000 --- a/lib/dataread_yongan.py +++ /dev/null @@ -1,1733 +0,0 @@ - -# 导入模块 -import pandas as pd -import numpy as np -import datetime -import string -import base64 -import requests -import random -import time -import re -import os -import hmac -import hashlib -import json -import math -import torch -torch.set_float32_matmul_precision("high") -import matplotlib.pyplot as plt -#设置plt显示中文 -plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 -plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 - -from datetime import timedelta -from sklearn import metrics -from reportlab.pdfbase import pdfmetrics # 注册字体 -from reportlab.pdfbase.ttfonts import TTFont # 字体类 -from reportlab.platypus import Table, SimpleDocTemplate, Paragraph, Image # 报告内容相关类 -from reportlab.lib.pagesizes import letter # 页面的标志尺寸(8.5*inch, 11*inch) -from reportlab.lib.styles import getSampleStyleSheet # 文本样式 -from reportlab.lib import colors # 颜色模块 -from reportlab.graphics.charts.barcharts import VerticalBarChart # 图表类 -from reportlab.graphics.charts.legends import Legend # 图例类 -from reportlab.graphics.shapes import Drawing # 绘图工具 -from reportlab.lib.units import cm # 单位：cm - -# 注册字体(提前准备好字体文件, 如果同一个文件需要多种字体可以注册多个) -pdfmetrics.registerFont(TTFont('SimSun', 'SimSun.ttf')) -#设置plt显示中文 -plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 -plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 - - -from config_yongan import * - - - - -# 定义函数 -def loadcsv(filename): - """ - 读取指定文件名的 CSV 文件。 - 如果文件编码为 UTF-8，则使用 UTF-8 编码读取；否则，使用 GBK 编码读取。 - - 参数: - filename (str): 要读取的 CSV 文件的文件名。 - - 返回: - pandas.DataFrame: 读取的数据。 - - """ - # 读取csv文件 - try: - df = pd.read_csv(filename, encoding='utf-8') - except UnicodeDecodeError: - df = pd.read_csv(filename, encoding='gbk') - return df - - -def dateConvert(df, datecol='ds'): - """ - 将数据框 df 中的 datecol 列转换为日期时间类型。 - - 参数: - df (pandas.DataFrame): 要转换的 DataFrame。 - datecol (str): 要转换的列名，默认为 'ds'。 - - 返回: - pandas.DataFrame: 转换后的 DataFrame。 - - """ - # 将date列转换为datetime类型 - try: - df[datecol] = pd.to_datetime(df[datecol],format=r'%Y-%m-%d') - except: - df[datecol] = pd.to_datetime(df[datecol],format=r'%Y/%m/%d') - return df - - -def calculate_kdj(data, n=9): - ''' - 给传进来的df 添加列： 波动率，最高，最低，k,d ，j - ''' - # 对数据按照日期升序排序 - data = data.sort_values(by='ds', ascending=True) - # 因为没有高开低价格，利用每日波动率模拟当天最高价和最低价 - data['pctchange'] = data['y'].pct_change() - # 收益为0的用0.01 - data['pctchange'] = data['pctchange'].replace(0,0.01) - # 去除空值 - data.dropna(inplace=True) - # 重置索引 - data.reset_index(drop=True,inplace=True) - # 计算最高价和最低价 - data['high'] = data['y']* (1+abs(data['pctchange'])/2) - data['low'] = data['y']* (1-abs(data['pctchange'])/2) - # 计算n日内最低价 - low_list = data['y'].rolling(window=n, min_periods=1).min() - # 计算n日内最高价 - high_list = data['y'].rolling(window=n, min_periods=1).max() - # 计算未成熟随机值 - rsv = ((data['y'] - low_list) / (high_list - low_list)) * 100 - # 初始化k值为50 - k = pd.Series(50, index=data.index) - # 初始化d值为50 - d = pd.Series(50, index=data.index) - # 计算k值和d值 - for i in range(1, len(data)): - k[i] = (2/3 * k[i - 1]) + (1/3 * rsv[i]) - d[i] = (2/3 * d[i - 1]) + (1/3 * k[i]) - # 计算j值 - j = 3 * k - 2 * d - - # 将k值、d值和j值添加到数据中 - data['K'] = k - data['D'] = d - data['J'] = j - # 将包含 KDJ 指标的数据保存到新的 CSV 文件 - data.to_csv('stock_data_with_kdj.csv', index=False) - # data = data.dropna() - return data - - -# 上传报告 -def get_head_auth_report(): - """ - 通过 POST 请求登录到指定的 URL，并从响应中获取认证令牌。 - - 返回: - str: 如果登录成功，返回认证令牌；否则返回 None。 - """ - logger.info("获取token中...") - logger.info(f'url:{login_pushreport_url},login_data:{login_data}') - # 发送 POST 请求到登录 URL，携带登录数据 - login_res = requests.post(url=login_pushreport_url, json=login_data, timeout=(3, 30)) - - # 将响应内容转换为 JSON 格式 - text = json.loads(login_res.text) - logger.info(f'token接口响应：{text}') - # 如果响应状态为成功 - if text["status"]: - # 从响应数据中获取认证令牌 - token = text["data"]["accessToken"] - # 返回认证令牌 - return token - - -def upload_report_data(token, upload_data): - """ - 上传报告数据到指定的URL - - 参数: - token (str): 认证令牌 - upload_data (dict): 要上传的报告数据，包含必要的字段和信息 - - 返回: - dict: 如果上传成功，返回响应对象；否则返回None - """ - # 直接使用传入的 upload_data - upload_data = upload_data - - # 设置请求头部 - headers = {"Authorization": token} - - # 打印日志，显示正在上传报告数据 - logger.info("报告上传中...") - - # 打印日志，显示认证头部信息 - logger.info(f"token:{token}") - - # 打印日志，显示要上传的报告数据 - logger.info(f"upload_data:{upload_data}" ) - - # 发送POST请求，上传报告数据 - upload_res = requests.post(url=upload_url, headers=headers, json=upload_data, timeout=(3, 15)) - - # 将响应内容转换为 JSON 格式 - upload_res = json.loads(upload_res.text) - - # 打印日志，显示响应内容 - logger.info(upload_res) - - # 如果上传成功，返回响应对象 - if upload_res: - return upload_res - # 如果上传失败，打印日志并返回None - else: - logger.info("报告上传失败") - return None - - -def upload_warning_data(warning_data): - """ - 上传预警数据到指定的URL - - 参数: - warning_data (dict): 要上传的预警数据，包含必要的字段和信息 - - 返回: - requests.Response: 如果上传成功，返回响应对象；否则返回None - """ - # 获取认证头部信息 - token = get_head_auth_report() - - # 设置请求头部 - headers = {"Authorization": token} - - # 打印日志，显示正在上传预警数据 - logger.info("预警上传中...") - - # 打印日志，显示上传的URL - logger.info(f"upload_warning_url:{upload_warning_url}") - - # 打印日志，显示认证头部信息 - logger.info(f"token:{token}") - - # 打印日志，显示要上传的预警数据 - logger.info(f"warning_data:{warning_data}") - - # 发送POST请求，上传预警数据 - upload_res = requests.post(url=upload_warning_url, headers=headers, json=warning_data, timeout=(3, 15)) - - # 如果上传成功，返回响应对象 - if upload_res: - return upload_res - # 如果上传失败，打印日志并返回None - else: - logger.info("预警上传失败") - return None - - - -def upload_warning_info(df_count): - """ - 上传预警信息到指定的URL - - 参数: - df_count (int): 停更的数量 - - 返回: - None - """ - # 打印日志，显示正在上传预警信息 - logger.info(f'上传预警信息') - - try: - # 获取当前日期 - warning_date = datetime.datetime.now().strftime('%Y-%m-%d') - - # 构建预警内容 - content = f'{warning_date}有{df_count}个停更' - - # 更新预警数据中的日期和内容 - warning_data['data']['WARNING_DATE'] = warning_date - warning_data['data']['WARNING_CONTENT'] = content - - # 调用 upload_warning_data 函数上传预警数据 - upload_warning_data(warning_data) - - # 打印日志，显示上传预警信息成功 - logger.info(f'上传预警信息成功') - except Exception as e: - # 打印日志，显示上传预警信息失败，并记录异常信息 - logger.error(f'上传预警信息失败：{e}') - - - -def create_feature_last_update_time(df): - """ - 计算特征停更信息用 - 参数: - df (DataFrame): 包含特征数据的 DataFrame - 返回: - DataFrame: 包含特征停更信息的 DataFrame - str: y 列的最后更新时间 - """ - df1 = df.copy() - # 找到每列的最后更新时间 - df1.set_index('ds', inplace=True) - last_update_times = df1.apply(lambda x: x.dropna().index.max().strftime('%Y-%m-%d') if not x.dropna().empty else None) - - # 保存每列的最后更新时间到文件 - last_update_times_df = pd.DataFrame(columns = ['feature', 'last_update_time','is_value','update_period','warning_date','stop_update_period']) - - # 打印每列的最后更新时间 - for column, last_update_time in last_update_times.items(): - values = [] - # 判断是不是常数值 - if df1[column].tail(20).nunique() == 1: - values = values + [column, last_update_time,1] - else: - values = values + [column, last_update_time,0] - # 计算特征数据值的时间差 - try: - # 计算预警日期 - time_diff = (df1[column].dropna().index.to_series().diff().mode()[0]).total_seconds() / 3600 / 24 - last_update_time_datetime = datetime.datetime.strptime(last_update_time, '%Y-%m-%d') - last_update_date = end_time if end_time != '' else datetime.datetime.now().strftime('%Y-%m-%d') - end_time_datetime = datetime.datetime.strptime(last_update_date, '%Y-%m-%d') - early_warning_date = last_update_time_datetime + timedelta(days=time_diff)*2 + timedelta(days=1) - stop_update_period = int(math.ceil((end_time_datetime-last_update_time_datetime).days / time_diff)) - early_warning_date = early_warning_date.strftime('%Y-%m-%d') - except KeyError: - time_diff = 0 - early_warning_date = end_time - continue - - values = values + [time_diff,early_warning_date,stop_update_period] - last_update_times_df.loc[len(last_update_times_df)] = values - - logger.info(f"Column {column} was last updated at {last_update_time}") - y_last_update_time = last_update_times_df[last_update_times_df['feature']=='y']['warning_date'].values[0] - last_update_times_df.to_csv(os.path.join(dataset,'last_update_times.csv'), index=False) - logger.info('特征停更信息保存到文件：last_update_times.csv') - return last_update_times_df,y_last_update_time - - - -# 统计特征频度 -def featurePindu(dataset): - # 读取文件 - df = loadcsv(os.path.join(dataset,'未填充的特征数据.csv')) - df['ds'] = pd.to_datetime(df['ds']) - # 按ds正序排序，重置索引 - df = df.sort_values(by='ds', ascending=True).reset_index(drop=True) - - # 统计特征频度 - # 每列随机抽取10个值，计算出5个时间间隔，统计每个时间间隔的频度 - columns = df.columns.to_list() - columns.remove('ds') - count_dict = {} - for column in columns: - # 获取每列时间间隔 - values = df[[column,'ds']] - values.dropna(inplace=True,axis=0) - values=values.reset_index(drop=True) - - # 抽取20%个值 - value = values.sample(frac=0.2) - index = value.index - next_index = index + 1 - count = [] - for i,j in zip(index, next_index): - #通过索引计算日期差 - try: - count.append((values.loc[j,'ds'] - values.loc[i,'ds']).days) - except: - pass - # 把31 换成 30 - count = [30 if i == 31 else i for i in count] - # 保留count中出现次数最多的数 - try: - count = max(set(count), key=count.count) - except ValueError : - logger.info(f'{column}列数据为空') - continue - # 存储到字典中 - count_dict[column] = count - - df = pd.DataFrame(count_dict,index=['count']).T - pindu_dfs = pd.DataFrame() - # 根据count分组 - # 输出特征频度统计 - pindudict = {'1':'日度','3':'日度','7':'周度','30':'月度','90':'季度','180':'半年度','365':'年度'} - for i in df.groupby('count'): - # 获取 i[1] 的索引值 - index = i[1].index - pindu_df = pd.DataFrame() - try: - pindu_df[pindudict[str(i[0])]+f'({len(i[1])})'] = index - except KeyError : - pindu_df[str(i[0])+f'天({len(i[1])})'] = index - # 合并到pindu_dfs - pindu_dfs = pd.concat([pindu_dfs,pindu_df],axis=1) - # nan替换为 ' ' - pindu_dfs = pindu_dfs.fillna('') - pindu_dfs.to_csv(os.path.join(dataset,'特征频度统计.csv'),index=False) - logger.info(pindu_dfs) - featureInfo = f'特征信息：总共有{len(columns)-2}个' - for i in pindu_dfs.columns: - featureInfo += f',{i}' - - featureInfo += ', 详看 附1、特征列表' - - featureInfo += ''' - 数据特征工程： - 1. 数据日期排序，新日期在最后 - 2. 删除空列，特征数据列没有值，就删除 - 3. 删除近两月不再更新值的指标 - 4. 非日度数据填充为日度数据，填充规则： - -- 向后填充，举例：假设周五出现一个周度指标数据，那么在这之前的数据用上周五的数据 - -- 向前填充，举例：采集数据开始日期为2018年1月1日，那么周度数据可能是2018年1月3日，那么3日的数据向前填充，使1日2日都有数值 - 数据特征相关性分析： - ''' - logger.info(featureInfo) - with open(os.path.join(dataset,'特征频度统计.txt'), 'w', encoding='utf-8') as f: - f.write(featureInfo) - logger.info('*'*200) - - -def featureAnalysis(df,dataset,y): - # 特征筛选 - import matplotlib.pyplot as plt - # 选择特征和标签列 - X = df.drop(['ds', 'y'], axis=1) # 特征集，排除时间戳和标签列 - yy = df['y'] # 标签集 - - # 标签集自相关函数分析 - from statsmodels.graphics.tsaplots import plot_acf - plot_acf(yy, lags=30) - plt.savefig(os.path.join(dataset,'指标数据自相关图.png')) - plt.close() - - # 标签集偏自相关函数分析 - from statsmodels.graphics.tsaplots import plot_pacf - plot_pacf(yy, lags=30) - plt.savefig(os.path.join(dataset,'指标数据偏自相关图.png')) - plt.close() - - # 画 特征与价格散点图 - # 删除所有*散点图.png - for file in os.listdir(dataset): - if file.endswith("散点图.png"): - os.remove(os.path.join(dataset, file)) - plt.rcParams['font.sans-serif'] = ['SimHei'] - plt.rcParams['axes.unicode_minus'] = False - plt.figure(figsize=(10, 10)) - # # 遍历X每一列，和yy画散点图 ， - # for i, col in enumerate(X.columns): - # plt.subplot(2, 2, i%4+1) - # plt.scatter(X[col], yy) - # plt.xlabel(col) - # plt.ylabel(y) - # plt.title(col) - # if i % 4 == 3 or i == len(X.columns)-1: - # plt.tight_layout() - # plt.savefig(os.path.join(dataset,f'{i}指标数据特征与价格散点图.png')) - # plt.close() - - - -def corr_feature(df): - # 重新命名列名，列名排序,y在第一个 - df.reindex(['y'] + sorted(df.columns.difference(['y']))) - df_test = df.copy() - # 取最后的220行 - df_test = df_test.tail(220) - # 去掉日期列 - df_test = df_test.drop(columns=['ds']) - # 不参与标准化 - df_test_noscaler = df_test.copy() # 滞后处理备份 - df_noscaler = df_test.copy() - # 画出相关性热力图 - df_test.to_csv(os.path.join(dataset,'同步相关性.csv')) - corr = df_test.corr() - # 保存相关系数 - corr.to_csv(os.path.join(dataset,'同步相关性系数.csv')) - # plt.figure(figsize=(10, 10)) - # sns.heatmap(corr, annot=True, cmap='coolwarm') - # plt.savefig('dataset/同步相关性热力图.png') - # plt.show() - - # 读取滞后周期文件，更改特征 - characteristic_period = pd.read_csv('dataset/特征滞后周期.csv',encoding='utf-8') - # 去掉周期为0的行 - characteristic_period = characteristic_period.drop(characteristic_period[characteristic_period['滞后周期'] == 0].index) - for col in df.columns: - # 跳过y列 - if col in ['y']: - continue - # 特征滞后n个周期，计算与y的相关性 - if col in characteristic_period['特征'].values: - # 获取特征对应的周期 - period = characteristic_period[characteristic_period['特征'] == col]['滞后周期'].values[0] - # 滞后处理 - df[col] = df[col].shift(period) - df.to_csv(os.path.join(dataset,'滞后处理后的数据集.csv')) - - - # corr_feture_noscaler = {} # 保存相关性最大的周期 - # 遍历df_test的每一列，计算相关性 - # for col in df_noscaler.columns: - # # 跳过y列 - # if col in ['y']: - # continue - # logger.info('特征：', col) - # # 特征滞后n个周期，计算与y的相关性 - # corr_dict = {} - # try: - # for i in range(0, 200): - # if i == 0: - # df_noscaler[col+'_'+str(i)] = df_noscaler[col] - # else: - # df_noscaler[col+'_'+str(i)] = df_noscaler[col].shift(i) - # corr_dict[col+'_'+str(i)] = abs(df_noscaler[col+'_'+str(i)].corr(df_noscaler['y'])) - # except : - # logger.info('特征：', col, '滑动错误，请查看') - # continue - # 输出相关性最大的特征 - # logger.info(max(corr_dict, key=corr_dict.get), corr_dict[max(corr_dict, key=corr_dict.get)]) - # corr_feture_noscaler[col] = max(corr_dict, key=corr_dict.get).split('_')[-1] - # 画出最相关性最大的特征和y的折线图 - # plt.figure(figsize=(10, 5)) - # plt.plot(df_noscaler[max(corr_dict, key=corr_dict.get)], label=max(corr_dict, key=corr_dict.get)) - # # 设置双坐标轴 - # ax1 = plt.gca() - # ax2 = ax1.twinx() - # ax2.plot(df_noscaler['y'], color='r', label='y') - # plt.legend() - # try: - # plt.savefig('dataset/特征与y的折线图_'+max(corr_dict, key=corr_dict.get)+'.png') - # except : - # # ：替换成_ - # plt.savefig('dataset/特征与y的折线图_'+max(corr_dict, key=corr_dict.get).replace(':','_').replace('/','_').replace('(','_').replace(')','_')+'.png') - # plt.close() - # 结果保存到txt文件 - # logger.info('不参与标准化的特征滞后相关性写入txt文件') - # with open('dataset/不参与标准化的特征滞后相关性.txt', 'w') as f: - # for key, value in corr_feture_noscaler.items(): - # f.write('%s:%s\n' % (key, value)) - # 遍历corr_feture_noscaler,更改df - # colnames_noscaler = [] - # for col in corr_feture_noscaler: - # colname = col+'_'+corr_feture_noscaler[col] - # if int(corr_feture_noscaler[col]) == 0: - # continue - # df_test_noscaler[colname] = df_test_noscaler[col].shift(int(corr_feture_noscaler[col])) - # df_test_noscaler = df_test_noscaler.drop(columns=[col]) - # colnames_noscaler.append(colname) - # 去除有空值的行 - # df_test_noscaler = df_test_noscaler.dropna() - # df_test_noscaler.reindex(['y'] + sorted(df_test_noscaler.columns.difference(['y']))) - # df_test_noscaler.to_csv('dataset/不参与标准化的特征滞后相关性.csv', index=False) - # 画出相关性热力图 - # corr = df_test_noscaler.corr() - # 保存相关系数 - # corr.to_csv(os.path.join(dataset,'不参与标准化的特征滞后相关性系数.csv')) - # plt.figure(figsize=(10, 10)) - # sns.heatmap(corr, annot=True, cmap='coolwarm') - # plt.savefig('dataset/不参与标准化的特征滞后相关性热力图.png') - # plt.close() - # # 标准化每列 - # from sklearn.preprocessing import StandardScaler - # scaler = StandardScaler() - # df_test = pd.DataFrame(scaler.fit_transform(df_test), columns=df_test.columns) - # corr_feture = {} # 保存相关性最大的周期 - # # 遍历df_test的每一列，计算相关性 - # for col in df_test.columns: - # # 跳过y列 - # if col == 'y': - # continue - # logger.info('特征：', col) - # # 特征滞后n个周期，计算与y的相关性 - # corr_dict = {} - # try: - # for i in range(0, 200): - # if i == 0: - # df_test[col+'_'+str(i)] = df_test[col] - # else: - # df_test[col+'_'+str(i)] = df_test[col].shift(i) - # corr_dict[col+'_'+str(i)] = abs(df_test[col+'_'+str(i)].corr(df_test['y'])) - # except : - # logger.info('特征：', col, '滑动错误，请查看') - # continue - # # 输出相关性最大的特征 - # logger.info(max(corr_dict, key=corr_dict.get), corr_dict[max(corr_dict, key=corr_dict.get)]) - # corr_feture[col] = max(corr_dict, key=corr_dict.get).split('_')[-1] - - - # # 结果保存到txt文件 - # with open('dataset/标准化的特征滞后相关性.txt', 'w') as f: - # for key, value in corr_feture.items(): - # f.write('%s:%s\n' % (key, value)) - # # 遍历corr_feture,更改df - # colnames = [] - # for col in corr_feture: - # colname = col+'_'+corr_feture[col] - # if int(corr_feture[col]) == 0: - # continue - # df[colname] = df[col].shift(int(corr_feture[col])) - # df = df.drop(columns=[col]) - # colnames.append(colname) - # # 去除有空值的行 - # df = df.dropna() - # df.reindex(['y'] + sorted(df.columns.difference(['y']))) - # df.to_csv('dataset/标准化后的特征滞后相关性.csv', index=False) - # # 画出相关性热力图 - # ds = df['ds'] - # df = df.drop(columns=['ds']) - # corr = df.corr() - # # 保存相关系数 - # corr.to_csv(os.path.join(dataset,'标准化后的特征滞后相关性系数.csv')) - # plt.figure(figsize=(10, 10)) - # sns.heatmap(corr, annot=True, cmap='coolwarm') - # plt.savefig('dataset/标准化后的特征滞后相关性热力图.png') - # plt.show() - # df['ds'] = ds - - # 去除nan值 - df = df.dropna() - return df - - -def calculate_kdj(data, n=9): - ''' - 给传进来的df 添加列： 波动率，最高，最低，k ,d ，j - ''' - data = data.sort_values(by='ds', ascending=True) - # 因为没有高开低价格，利用每日波动率模拟当天最高价和最低价 - data['pctchange'] = data['y'].pct_change() - # 收益为0的用0.01 - data['pctchange'] = data['pctchange'].replace(0,0.01) - data.dropna(inplace=True) - # 重置索引 - data.reset_index(drop=True,inplace=True) - data['high'] = data['y']* (1+abs(data['pctchange'])/2) - data['low'] = data['y']* (1-abs(data['pctchange'])/2) - low_list = data['y'].rolling(window=n, min_periods=1).min() - high_list = data['y'].rolling(window=n, min_periods=1).max() - rsv = ((data['y'] - low_list) / (high_list - low_list)) * 100 - k = pd.Series(50, index=data.index) - d = pd.Series(50, index=data.index) - for i in range(1, len(data)): - k[i] = (2/3 * k[i - 1]) + (1/3 * rsv[i]) - d[i] = (2/3 * d[i - 1]) + (1/3 * k[i]) - j = 3 * k - 2 * d - - data['K'] = k - data['D'] = d - data['J'] = j - # 将包含 KDJ 指标的数据保存到新的 CSV 文件 - data.to_csv('dataset\stock_data_with_kdj.csv', index=False) - # data = data.dropna() - return data - -def check_column(df,col_name,two_months_ago): - ''' - 检查列是否需要删除。 - 该函数会检查列是否为空值列、180天没有更新的列或常数值列。 - 参数: - col_name (str): 列名。 - df (DataFrame): 包含列的 DataFrame。 - 返回: - bool: 如果列需要删除，返回 True；否则，返回 False。 - ''' - if 'ds' in col_name or 'y' in col_name: - return False - df_check_column = df[['ds',col_name,'y']] - df_check_column = df_check_column.dropna() - - if len(df_check_column) == 0: - print(f'空值列：{col_name}') - return True - # 判断是不是常数列 - if df_check_column[(df_check_column['ds']>= two_months_ago)].groupby(col_name).ngroups < 2: - print(f'180没有更新：{col_name}') - return True - - # 判断相关系数大于0.6 - if is_del_corr > 0: - if abs(df_check_column[col_name].corr(df_check_column['y'])) < is_del_corr: - print(f'相关系数小于0.6：{col_name}') - return True - - corresponding_date = df_check_column.iloc[-1]['ds'] - return corresponding_date < two_months_ago - -def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y',dataset='dataset',delweekenday=False,add_kdj=False,is_timefurture=False): - ''' - 原油特征数据处理函数， - 接收的是两个df，一个是指标数据，一个是指标列表 - 输出的是一个df，包含ds，y，指标列 - ''' - df = df_zhibiaoshuju.copy() - - if end_time == '': - end_time = datetime.datetime.now().strftime('%Y-%m-%d') - # 重命名时间列，预测列 - df.rename(columns={datecol:'ds'},inplace=True) - df.rename(columns={y:'y'},inplace=True) - # 按时间顺序排列 - df.sort_values(by='ds',inplace=True) - df['ds'] = pd.to_datetime(df['ds']) - # 获取start_year年到end_time的数据 - df = df[df['ds'].dt.year >= start_year] - df = df[df['ds'] <= end_time] - # last_update_times_df,y_last_update_time = create_feature_last_update_time(df) - # logger.info(f'删除预警的特征前数据量：{df.shape}') - # columns_to_drop = last_update_times_df[last_update_times_df['warning_date'] < y_last_update_time ]['feature'].values.tolist() - # df = df.drop(columns = columns_to_drop) - # logger.info(f'删除预警的特征后数据量：{df.shape}') - # if is_update_warning_data: - # upload_warning_info(last_update_times_df,y_last_update_time) - # 去掉近最后数据对应的日期在六月以前的列，删除近2月的数据是常熟的列 - if is_del_tow_month: - current_date = datetime.datetime.now() - two_months_ago = current_date - timedelta(days=180) - logger.info(f'删除两月不更新特征前数据量：{df.shape}') - columns_to_drop = [] - for clo in df.columns: - if check_column(df,clo,two_months_ago): - columns_to_drop.append(clo) - df = df.drop(columns=columns_to_drop) - - logger.info(f'删除两月不更新特征后数据量：{df.shape}') - - if freq == 'W': - # 按周取样 - df = df.resample('W', on='ds').mean().reset_index() - elif freq == 'M': - # 按月取样 - df = df.resample('M', on='ds').mean().reset_index() - # 删除预测列空值的行 - df = df.dropna(subset=['y']) - logger.info(f'删除预测列为空值的行后数据量：{df.shape}') - df = df.dropna(axis=1, how='all') - logger.info(f'删除全为空值的列后数据量：{df.shape}') - df.to_csv(os.path.join(dataset,'未填充的特征数据.csv'),index=False) - # 去掉指标列表中的columns_to_drop的行 - df_zhibiaoliebiao = df_zhibiaoliebiao[df_zhibiaoliebiao['指标名称'].isin(df.columns.tolist())] - df_zhibiaoliebiao.to_csv(os.path.join(dataset,'特征处理后的指标名称及分类.csv'),index=False) - # 数据频度分析 - featurePindu(dataset=dataset) - # 向上填充 - df = df.ffill() - # 向下填充 - df = df.bfill() - - # 删除周六日的数据 - if delweekenday: - df = df[df['ds'].dt.weekday < 5] - - # kdj指标 - if add_kdj: - df = calculate_kdj(df) - # 衍生时间特征 - if is_timefurture: - df = addtimecharacteristics(df=df,dataset=dataset) - # 特征分析 - featureAnalysis(df,dataset=dataset,y=y) - return df - -def datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y',dataset='dataset',delweekenday=False,add_kdj=False,is_timefurture=False): - ''' - 聚烯烃特征数据处理函数， - 接收的是两个df，一个是指标数据，一个是指标列表 - 输出的是一个df，包含ds，y，指标列 - ''' - df = df_zhibiaoshuju.copy() - if end_time == '': - end_time = datetime.datetime.now().strftime('%Y-%m-%d') - # date转为pddate - df.rename(columns={datecol:'ds'},inplace=True) - - # 指定列统一减少数值 - df[offsite_col] = df[offsite_col]-offsite - # 预测列为avg_cols的均值 - df[y] = df[avg_cols].mean(axis=1) - # 去掉多余的列avg_cols - df = df.drop(columns=avg_cols) - - # 重命名预测列 - df.rename(columns={y:'y'},inplace=True) - # 按时间顺序排列 - df.sort_values(by='ds',inplace=True) - df['ds'] = pd.to_datetime(df['ds']) - # 获取2018年到当前日期的数据 - df = df[df['ds'].dt.year >= 2018] - # 获取小于等于当前日期的数据 - df = df[df['ds'] <= end_time] - logger.info(f'删除两月不更新特征前数据量：{df.shape}') - # 去掉近最后数据对应的日期在两月以前的列，删除近2月的数据是常数的列 - current_date = datetime.datetime.now() - two_months_ago = current_date - timedelta(days=40) - # 检查两月不更新的特征 - def check_column(col_name): - if 'ds' in col_name or 'y' in col_name: - return False - df_check_column = df[['ds',col_name]] - df_check_column = df_check_column.dropna() - if len(df_check_column) == 0: - return True - if df_check_column[(df_check_column['ds']>= two_months_ago)].groupby(col_name).ngroups < 2: - return True - corresponding_date = df_check_column.iloc[-1]['ds'] - return corresponding_date < two_months_ago - columns_to_drop = df.columns[df.columns.map(check_column)].tolist() - df = df.drop(columns = columns_to_drop) - - logger.info(f'删除两月不更新特征后数据量：{df.shape}') - - # 删除预测列空值的行 - df = df.dropna(subset=['y']) - logger.info(f'删除预测列为空值的行后数据量：{df.shape}') - df = df.dropna(axis=1, how='all') - logger.info(f'删除全为空值的列后数据量：{df.shape}') - df.to_csv(os.path.join(dataset,'未填充的特征数据.csv'),index=False) - # 去掉指标列表中的columns_to_drop的行 - df_zhibiaoliebiao = df_zhibiaoliebiao[df_zhibiaoliebiao['指标名称'].isin(df.columns.tolist())] - df_zhibiaoliebiao.to_csv(os.path.join(dataset,'特征处理后的指标名称及分类.csv'),index=False) - # 频度分析 - featurePindu(dataset=dataset) - # 向上填充 - df = df.ffill() - # 向下填充 - df = df.bfill() - - # 删除周六日的数据 - if delweekenday: - df = df[df['ds'].dt.weekday < 5] - - if add_kdj: - df = calculate_kdj(df) - - if is_timefurture: - df = addtimecharacteristics(df=df,dataset=dataset) - - featureAnalysis(df,dataset=dataset,y=y) - return df - -def getdata(filename, datecol='date',y='y',dataset='',add_kdj=False,is_timefurture=False,end_time=''): - logger.info('getdata接收：'+filename+' '+datecol+' '+end_time) - # 判断后缀名 csv或excel - if filename.endswith('.csv'): - df = loadcsv(filename) - else: - # 读取excel 指标数据 - df_zhibiaoshuju = pd.read_excel(filename,sheet_name='指标数据') - df_zhibiaoliebiao = pd.read_excel(filename,sheet_name='指标列表') - - - # 日期字符串转为datatime - df = datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol,y = y,dataset=dataset,add_kdj=add_kdj,is_timefurture=is_timefurture,end_time=end_time) - - return df,df_zhibiaoliebiao -def getdata_juxiting(filename, datecol='date',y='y',dataset='',add_kdj=False,is_timefurture=False,end_time=''): - logger.info('getdata接收：'+filename+' '+datecol+' '+end_time) - # 判断后缀名 csv或excel - if filename.endswith('.csv'): - df = loadcsv(filename) - else: - # 读取excel 指标数据 - df_zhibiaoshuju = pd.read_excel(filename,sheet_name='指标数据') - df_zhibiaoliebiao = pd.read_excel(filename,sheet_name='指标列表') - - # 日期字符串转为datatime - df = datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,datecol,y = y,dataset=dataset,add_kdj=add_kdj,is_timefurture=is_timefurture,end_time=end_time) - - return df - - -def sanitize_filename(filename): - # 使用正则表达式替换不合规的字符 - # 这里我们替换为下划线'_'，但你可以根据需要选择其他字符 - sanitized = re.sub(r'[\\/*?:"<>|\s]', '_', filename) - # 移除开头的点（在某些系统中，以点开头的文件可能是隐藏的） - sanitized = re.sub(r'^\.', '', sanitized) - # 如果需要，可以添加更多替换规则 - return sanitized - -class BinanceAPI: - ''' - 获取 Binance API 请求头签名 - ''' - def __init__(self, APPID, SECRET): - self.APPID = APPID - self.SECRET = SECRET - self.get_signature() - - # 生成随机字符串作为 nonce - def generate_nonce(self, length=32): - self.nonce = ''.join(random.choices(string.ascii_letters + string.digits, k=length)) - return self.nonce - - # 获取当前时间戳（秒） - def get_timestamp(self): - return int(time.time()) - - # 构建待签名字符串 - def build_sign_str(self): - return f'appid={self.APPID}&nonce={self.nonce}×tamp={self.timestamp}' - - # 使用 HMAC SHA-256 计算签名 - def calculate_signature(self, secret, message): - return base64.urlsafe_b64encode(hmac.new(secret.encode('utf-8'), message.encode('utf-8'), hashlib.sha256).digest()).decode('utf-8') - - def get_signature(self): - # 调用上述方法生成签名 - self.nonce = self.generate_nonce() - self.timestamp = self.get_timestamp() - self.sign_str = self.build_sign_str() - self.signature = self.calculate_signature(self.SECRET, self.sign_str) - # return self.signature - -class Graphs: - # 绘制标题 - @staticmethod - def draw_title(title: str): - # 获取所有样式表 - style = getSampleStyleSheet() - # 拿到标题样式 - ct = style['Heading1'] - # 单独设置样式相关属性 - ct.fontName = 'SimSun' # 字体名 - ct.fontSize = 18 # 字体大小 - ct.leading = 50 # 行间距 - ct.textColor = colors.green # 字体颜色 - ct.alignment = 1 # 居中 - ct.bold = True - # 创建标题对应的段落，并且返回 - return Paragraph(title, ct) - - # 绘制小标题 - @staticmethod - def draw_little_title(title: str): - # 获取所有样式表 - style = getSampleStyleSheet() - # 拿到标题样式 - ct = style['Normal'] - # 单独设置样式相关属性 - ct.fontName = 'SimSun' # 字体名 - ct.fontSize = 15 # 字体大小 - ct.leading = 30 # 行间距 - ct.textColor = colors.red # 字体颜色 - # 创建标题对应的段落，并且返回 - return Paragraph(title, ct) - - # 绘制普通段落内容 - @staticmethod - def draw_text(text: str): - # 获取所有样式表 - style = getSampleStyleSheet() - # 获取普通样式 - ct = style['Normal'] - ct.fontName = 'SimSun' - ct.fontSize = 12 - ct.wordWrap = 'CJK' # 设置自动换行 - ct.alignment = 0 # 左对齐 - ct.firstLineIndent = 32 # 第一行开头空格 - ct.leading = 25 - return Paragraph(text, ct) - - # 绘制表格 - @staticmethod - def draw_table(*args): - # 列宽度 - col_width = args[0] - style = [ - ('FONTNAME', (0, 0), (-1, -1), 'SimSun'), # 字体 - ('FONTSIZE', (0, 0), (-1, 0), 12), # 第一行的字体大小 - ('FONTSIZE', (0, 1), (-1, -1), 10), # 第二行到最后一行的字体大小 - ('BACKGROUND', (0, 0), (-1, 0), '#d5dae6'), # 设置第一行背景颜色 - ('ALIGN', (0, 0), (-1, -1), 'CENTER'), # 第一行水平居中 - ('ALIGN', (0, 1), (-1, -1), 'LEFT'), # 第二行到最后一行左右左对齐 - ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'), # 所有表格上下居中对齐 - ('TEXTCOLOR', (0, 0), (-1, -1), colors.darkslategray), # 设置表格内文字颜色 - ('GRID', (0, 0), (-1, -1), 0.5, colors.grey), # 设置表格框线为grey色，线宽为0.5 - # ('SPAN', (0, 1), (0, 2)), # 合并第一列二三行 - # ('SPAN', (0, 3), (0, 4)), # 合并第一列三四行 - # ('SPAN', (0, 5), (0, 6)), # 合并第一列五六行 - # ('SPAN', (0, 7), (0, 8)), # 合并第一列五六行 - ] - table = Table(args[1:], colWidths=col_width, style=style) - return table - - # 创建图表 - @staticmethod - def draw_bar(bar_data: list, ax: list, items: list): - drawing = Drawing(500, 250) - bc = VerticalBarChart() - bc.x = 45 # 整个图表的x坐标 - bc.y = 45 # 整个图表的y坐标 - bc.height = 200 # 图表的高度 - bc.width = 350 # 图表的宽度 - bc.data = bar_data - bc.strokeColor = colors.black # 顶部和右边轴线的颜色 - bc.valueAxis.valueMin = 5000 # 设置y坐标的最小值 - bc.valueAxis.valueMax = 26000 # 设置y坐标的最大值 - bc.valueAxis.valueStep = 2000 # 设置y坐标的步长 - bc.categoryAxis.labels.dx = 2 - bc.categoryAxis.labels.dy = -8 - bc.categoryAxis.labels.angle = 20 - bc.categoryAxis.categoryNames = ax - - # 图示 - leg = Legend() - leg.fontName = 'SimSun' - leg.alignment = 'right' - leg.boxAnchor = 'ne' - leg.x = 475 # 图例的x坐标 - leg.y = 240 - leg.dxTextSpace = 10 - leg.columnMaximum = 3 - leg.colorNamePairs = items - drawing.add(leg) - drawing.add(bc) - return drawing - - # 绘制图片 - @staticmethod - def draw_img(path): - img = Image(path) # 读取指定路径下的图片 - img.drawWidth = 20*cm # 设置图片的宽度 - img.drawHeight = 10*cm # 设置图片的高度 - return img - -# 定义样式函数 -def style_row(row): - if '周' in row['频度']: - return ['background-color: yellow'] * len(row) - else: - return ['background-color: gray'] * len(row) - - - -class EtaReader(): - def __init__(self,signature,classifylisturl,classifyidlisturl,edbcodedataurl,edbcodelist,edbdatapushurl,edbdeleteurl,edbbusinessurl): - ''' - 初始化 EtaReader 类的实例。 - - 参数: - signature (str): 用于 API 请求的签名。 - classifylisturl (str): 分类列表的 URL。 - classifyidlisturl (str): 分类 ID 列表的 URL。 - edbcodedataurl (str): EDB 代码数据的 URL。 - edbdatapushurl (str): EDB 数据推送的 URL。 - edbcodelist (str): EDB 代码列表的 URL。 - edbdeleteurl (str): EDB 数据删除的 URL。 - edbbusinessurl (str): EDB 业务数据的 URL。 - - 返回: - None - ''' - self.signature = signature - self.classifylisturl = classifylisturl - self.classifyidlisturl = classifyidlisturl - self.edbcodedataurl = edbcodedataurl - self.edbdatapushurl = edbdatapushurl - self.edbcodelist = edbcodelist - self.edbdeleteurl = edbdeleteurl - self.edbbusinessurl = edbbusinessurl - - - def filter_yuanyou_data(self,ClassifyName,data): - ''' - 指标名称保留规则 - ''' - - # 包含 关键词 去除， 返回flase - if any(keyword in data for keyword in ['运费','检修','波动率','地缘政治','股价', - '同比','环比','环差','裂差','4WMA','变频','道琼斯','标普500','纳斯达克', - '四周均值','名占比','残差','DMA', - '连7-连9','4周平均','4周均值','滚动相关性','日本']): - return False - - # 检查需要的特征 - # 去掉 分析 分类下的数据 - if ClassifyName == '分析': - return False - - # 保留 库存中特殊关键词 - if ClassifyName == '库存': - if any(keyword in data for keyword in ['原油' , '美国' ,'全球' ,'中国' ,'富查伊拉','ARA' ]): - return True - else: - pass - else: - pass - - # 去掉 持仓中不是基金的数据 - if ClassifyName == '持仓': - if '基金' not in data: - return False - else: - pass - else: - pass - - # 去掉 航班中不是中国、美国 的数据 - if ClassifyName == '需求': - if '航班' in data : - if '中国' in data or '美国' in data : - return True - else: - return False - else: - pass - else: - pass - - # 分类为 期货市场，同质性数据取第一个 - if ClassifyName == '期货市场': - # 去掉c1-9 以后的 - if 'c1-c' in data: - try: - c = int(data.split('c1-c')[1]) - except: - return False - if c > 9 : - return False - else: - pass - - else: - pass - - # 判断 同质性数据， 字符串开头 - strstartdict = {'ICE Brent c':"ICE Brent c14", - 'NYMWX WTI c':"NYMWX WTI c5", - 'INE SC c':"INE SC c1", - 'EFS c':"EFS c", - 'Dubai Swap c':"Dubai Swap c1", - 'Oman Swap c':"Oman Swap c1", - 'DME Oman c':"DME Oman c1", - 'Murban Futures c':"Murban Futures c1", - 'Dubai连合约价格':'Dubai连1合约价格', - '美国RBOB期货月份合约价格':'美国RBOB期货2309月份合约价格', - 'Brent连合约价格':'Brent连1合约价格', - 'WTI连合约价格':'WTI连1合约价格', - '布伦特连合约价格':'Brent连1合约价格', - 'Brent 连合约价格':'Brent连1合约价格', - 'Dubai连合约价格':'Dubai连1合约价格', - 'Brent连':'Brent连1合约价格', - 'brent连':'Brent连1合约价格', - } - # 判断名称字符串开头是否在 strstartdict.keys中 - match = re.match(r'([a-zA-Z\s]+)(\d+)', data) - if match: - part1 = match.group(1) - part2 = match.group(2) - if part1 in [i for i in strstartdict.keys()]: - if data == strstartdict[part1]: - return True - else: - return False - # data = 'Brent 连7合约价格' - # 判断名称字符串去掉数字后是否在 strstartdict.keys中 - match = re.findall(r'\D+', data) - if match : - if len(match) == 2: - part1 = match[0] - part2 = match[1] - if part1+part2 in [i for i in strstartdict.keys()]: - if data == strstartdict[part1+part2]: - return True - else: - return False - else: - pass - elif len(match) == 1: - match = re.findall(r'\D+', data) - part1 = match[0] - - if part1 in [i for i in strstartdict.keys()]: - if data == strstartdict[part1]: - return True - else: - return False - else: - pass - else: - pass - - # 去掉kpler数据源 - if 'Kpler' in ClassifyName or 'kpler' in ClassifyName: - return False - - return True - - def filter_pp_data(self,ClassifyName,data): - ''' - 指标名称保留规则 - ''' - - # 包含 关键词 去除， 返回flase - # if any(keyword in data for keyword in ['运费','检修','波动率','地缘政治','股价', - # '同比','环比','环差','裂差','4WMA','变频','道琼斯','标普500','纳斯达克', - # '四周均值','名占比','残差','DMA', - # '连7-连9','4周平均','4周均值','滚动相关性','日本']): - # return False - # 包含 关键词 保留， 返回True - if any(keyword in data for keyword in ['拉丝']): - return True - - - - # 检查需要的特征 - # 去掉 期货市场 分类下的数据 - if ClassifyName == '期货市场': - return False - else: - pass - - # 保留 库存 下所有指标 - if ClassifyName == '库存': - return True - else: - pass - - # 保留 进出口 下所有指标 - if ClassifyName == '进出口': - return True - else: - pass - - # 保留 价差 下所有指标 - if ClassifyName == '价差': - return True - else: - pass - - # 保留 供应 下所有指标 - if ClassifyName == '供应': - return True - else: - pass - - - # 保留 需求 下所有指标 - if ClassifyName == '需求': - return True - else: - pass - - - return True - - # 通过edbcode 获取指标数据 - def edbcodegetdata(self,df,EdbCode,EdbName): - # 根据指标id，获取指标数据 - url = self.edbcodedataurl+str(EdbCode) - # 发送GET请求 - response = requests.get(url, headers=self.headers) - - # 检查响应状态码 - if response.status_code == 200: - data = response.json() # 假设接口返回的是JSON数据 - all_data_items = data.get('Data') - # 列表转换为DataFrame - df3 = pd.DataFrame(all_data_items, columns=['DataTime', 'Value', 'UpdateTime']) - # df3 = pd.read_json(all_data_items, orient='records') - - # 去掉UpdateTime 列 - df3 = df3.drop(columns=['UpdateTime']) - # df3.set_index('DataTime') - df3.rename(columns={'Value': EdbName}, inplace=True) - # 将数据存储df1 - df = pd.merge(df, df3, how='outer',on='DataTime',suffixes= ('', '_y')) - # 按时间排序 - df = df.sort_values(by='DataTime', ascending=True) - return df - - else: - # 请求失败，打印错误信息 - logger.info(f'Error: {response.status_code}, {response.text}') - # 主动抛出异常 - raise Exception(f'Error: {response.status_code}, {response.text}') - - def get_eta_api_yuanyou_data(self,data_set,dataset=''): - ''' - 从ETA API获取原油数据 - - 参数: - data_set (str): 数据集名称 - dataset (str): 数据集ID，默认为空 - - 返回: - None - ''' - today = datetime.date.today().strftime("%Y-%m-%d") - - # 定义你的headers，这里可以包含多个参数 - self.headers = { - 'nonce': self.signature.nonce, # 例如，一个认证令牌 - 'timestamp': str(self.signature.timestamp), # 自定义的header参数 - 'appid': self.signature.APPID, # 另一个自定义的header参数 - 'signature': self.signature.signature - } - - # 从列表数据中获取指标名称，判断指标名称频度是否为日 ，如果是，则获取UniqueCode，然后获取指标数据，保存到xlat文件中的sheet表。 - - ''' - df = sheetname 指标列表，存储 指标分类-指标名称-指标id-频度 - df1 = sheetname 指标数据 ,存储 时间-指标名称1-指标名称2... - - ''' - - # 构建新的DataFrame df df1 - df = pd.DataFrame(columns=['指标分类', '指标名称', '指标id', '频度','指标来源','来源id','最后更新时间','更新周期','预警日期','停更周期']) - df1 = pd.DataFrame(columns=['DataTime']) - - - # 外网环境无法访问，请确认是否为内网环境 - try: - # 发送GET请求 获取指标分类列表 - response = requests.get(self.classifylisturl, headers=self.headers) - except requests.exceptions.RequestException as e: - raise Exception(f"请求失败,请确认是否为内网环境: {e}","\033[0m") - - # 检查响应状态码 - if response.status_code == 200: - # 获取成功， 处理响应内容 - data = response.json() # 假设接口返回的是JSON数据 - - # 请求成功，处理响应内容 - # logger.info(data.get('Data')) - # 定义你想要保留的固定值 - fixed_value = 1214 - - # 遍历列表，只保留那些'category' key的值为固定值的数据项 - filtered_data = [item for item in data.get('Data') if item.get('ParentId') == fixed_value] - - #然后循环filtered_data去获取list数据，才能获取到想要获取的ClassifyId - n = 0 - for item in filtered_data: - n+= 1 - # if n>50: - # break - ClassifyId = item["ClassifyId"] #分类id，分类下的指标列表接口的请求参数 - ClassifyName = item["ClassifyName"] #分类名称，要保存到df的指标分类列 - # 根据分类id，获取指标列表 - url = self.classifyidlisturl+str(ClassifyId) - response = requests.get(url, headers=self.headers) - if response.status_code == 200: - # logger.info(response.text) - data2 = response.json() - Data = data2.get('Data') - for i in Data: - # s+= 1 - EdbCode = i.get('EdbCode') - EdbName = i.get('EdbName') # 指标名称，要保存到df2的指标名称列,df的指标名称列 - Frequency = i.get('Frequency') # 频度，要保存到df的频度列 - SourceName = i.get('SourceName') # 来源名称，要保存到df的频度列 - Source = i.get('Source') # 来源ID，要保存到df的频度列 - Unit = i.get('Unit') # 单位，要保存到df的单位列 - # 频度不是 日 或者 周的 跳过 - if Frequency not in ['日度','周度','日','周']: - continue - - # 只保留手工数据中，名称带有 海运出口 海运进口 - if Source == 9 and not ('海运出口' in EdbName or '海运进口' in EdbName): - continue - - # 不要wind数据 - if Source == 2: - continue - - - # 判断名称是否需要保存 - isSave = self.filter_yuanyou_data(ClassifyName,EdbName) - if isSave: - # 保存到df - df1 = self.edbcodegetdata(df1,EdbCode,EdbName) - # 取df1所有行最后一列 - edbname_df = df1[['DataTime',f'{EdbName}']] - edbname_df = edbname_df.dropna() - - if len(edbname_df) == 0: - logger.info(f'指标名称：{EdbName} 没有数据') - continue - try: - time_sequence = edbname_df['DataTime'].values.tolist()[-10:] - except IndexError: - time_sequence = edbname_df['DataTime'].values.tolist() - # 使用Counter来统计每个星期几的出现次数 - from collections import Counter - weekday_counter = Counter(datetime.datetime.strptime(time_str, "%Y-%m-%d").strftime('%A') for time_str in time_sequence) - - # 打印出现次数最多的星期几 - try: - most_common_weekday = weekday_counter.most_common(1)[0][0] - # 计算两周后的日期 - warning_date = (datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d") + datetime.timedelta(weeks=2)).strftime("%Y-%m-%d") - stop_update_period = (datetime.datetime.strptime(today, "%Y-%m-%d") - datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d")).days // 7 - - except IndexError: - most_common_weekday = '其他' - stop_update_period = 0 - if '日' in Frequency: - most_common_weekday = '每天' - warning_date = (datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d") + datetime.timedelta(days=3)).strftime("%Y-%m-%d") - stop_update_period = (datetime.datetime.strptime(today, "%Y-%m-%d") - datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d")).days - - # 保存频度 指标名称 分类 指标id 到 df - df2 = pd.DataFrame({'指标分类': ClassifyName, - '指标名称': EdbName, - '指标id': EdbCode, - '单位': Unit, - '频度': Frequency, - '指标来源':SourceName, - '来源id':Source, - '最后更新时间':edbname_df['DataTime'].values[-1], - '更新周期':most_common_weekday, - '预警日期':warning_date, - '停更周期':stop_update_period},index=[0], - ) - - # df = pd.merge(df, df2, how='outer') - df = pd.concat([df, df2]) - else: - logger.info(f'跳过指标 {EdbName}') - - # 找到列表中不在指标列中的指标id，保存成新的list - new_list = [item for item in self.edbcodelist if item not in df['指标id'].tolist()] - logger.info(new_list) - # 遍历new_list，获取指标数据，保存到df1 - for item in new_list: - logger.info(item) - # 将item 加入到 df['指标id']中 - try: - itemname = edbcodenamedict[item] - except: - itemname = item - - df1 = self.edbcodegetdata(df1,item,itemname) - df = pd.concat([df, pd.DataFrame({'指标分类': '其他', '指标名称': itemname, '指标id': item, '频度': '其他','指标来源':'其他','来源id':'其他'},index=[0])]) - - # 按时间排序 - df1.sort_values('DataTime',inplace=True,ascending=False) - df1.rename(columns={'DataTime': 'date'},inplace=True) - # df1.dropna(inplace=True) - # 去掉大于今天日期的行 - df1 = df1[df1['date'] <= datetime.datetime.now().strftime('%Y-%m-%d')] - logger.info(df1.head()) - # logger.info(f'{df1.head()}') - - df_zhibiaoshuju = df1.copy() - df_zhibiaoliebiao = df.copy() - return df_zhibiaoshuju,df_zhibiaoliebiao - - def get_eta_api_pp_data(self,data_set,dataset=''): - global ClassifyId - today = datetime.date.today().strftime("%Y-%m-%d") - - # 定义你的headers，这里可以包含多个参数 - self.headers = { - 'nonce': self.signature.nonce, # 例如，一个认证令牌 - 'timestamp': str(self.signature.timestamp), # 自定义的header参数 - 'appid': self.signature.APPID, # 另一个自定义的header参数 - 'signature': self.signature.signature - } - - # 从列表数据中获取指标名称，判断指标名称频度是否为日 ，如果是，则获取UniqueCode，然后获取指标数据，保存到xlat文件中的sheet表。 - - ''' - df = sheetname 指标列表，存储 指标分类-指标名称-指标id-频度 - df1 = sheetname 指标数据 ,存储 时间-指标名称1-指标名称2... - - ''' - - # 构建新的DataFrame df df1 - df = pd.DataFrame(columns=['指标分类', '指标名称', '指标id', '频度']) - df1 = pd.DataFrame(columns=['DataTime']) - - - # 外网环境无法访问，请确认是否为内网环境 - try: - # 发送GET请求 获取指标分类列表 - response = requests.get(self.classifylisturl, headers=self.headers) - except requests.exceptions.RequestException as e: - raise Exception(f"请求失败,请确认是否为内网环境: {e}","\033[0m") - - # 检查响应状态码 - if response.status_code == 200: - # 获取成功， 处理响应内容 - data = response.json() # 假设接口返回的是JSON数据 - - # 请求成功，处理响应内容 - # logger.info(data.get('Data')) - # 定义你想要保留的固定值 - fixed_value = ClassifyId - - # 遍历列表，只保留那些'category' key的值为固定值的数据项 - filtered_data = [item for item in data.get('Data') if item.get('ParentId') == fixed_value] - - #然后循环filtered_data去获取list数据，才能获取到想要获取的ClassifyId - n = 0 - for item in filtered_data: - n+= 1 - # if n>50: - # break - ClassifyId = item["ClassifyId"] #分类id，分类下的指标列表接口的请求参数 - ClassifyName = item["ClassifyName"] #分类名称，要保存到df的指标分类列 - # 根据分类id，获取指标列表 - url = self.classifyidlisturl+str(ClassifyId) - response = requests.get(url, headers=self.headers) - if response.status_code == 200: - # logger.info(response.text) - data2 = response.json() - Data = data2.get('Data') - for i in Data: - # s+= 1 - EdbCode = i.get('EdbCode') - EdbName = i.get('EdbName') # 指标名称，要保存到df2的指标名称列,df的指标名称列 - Frequency = i.get('Frequency') # 频度，要保存到df的频度列 - # 频度不是 日 或者 周的 跳过 - if Frequency not in ['日度','周度','日','周']: - continue - - # 判断名称是否需要保存 - isSave = self.filter_pp_data(ClassifyName,EdbName) - if isSave: - # 保存到df - # 保存频度 指标名称 分类 指标id 到 df - df2 = pd.DataFrame({'指标分类': ClassifyName, '指标名称': EdbName, '指标id': EdbCode, '频度': Frequency},index=[0]) - - # df = pd.merge(df, df2, how='outer') - df = pd.concat([df, df2]) - df1 = self.edbcodegetdata(df1,EdbCode,EdbName) - else: - logger.info(f'跳过指标 {EdbName}') - - # 找到列表中不在指标列中的指标id，保存成新的list - new_list = [item for item in self.edbcodelist if item not in df['指标id'].tolist()] - logger.info(new_list) - # 遍历new_list，获取指标数据，保存到df1 - for item in new_list: - logger.info(item) - # 将item 加入到 df['指标id']中 - try: - itemname = edbcodenamedict[item] - except: - itemname = item - - df1 = self.edbcodegetdata(df1,item,itemname) - df = pd.concat([df, pd.DataFrame({'指标分类': '其他', '指标名称': itemname, '指标id': item, '频度': '其他'},index=[0])]) - - # 按时间排序 - df1.sort_values('DataTime',inplace=True,ascending=False) - df1.rename(columns={'DataTime': 'date'},inplace=True) - # df1.dropna(inplace=True) - # 去掉大于今天日期的行 - df1 = df1[df1['date'] <= datetime.datetime.now().strftime('%Y-%m-%d')] - logger.info(df1.head()) - # logger.info(f'{df1.head()}') - # 保存到xlsx文件的sheet表 - with pd.ExcelWriter(os.path.join(dataset,data_set)) as file: - df1.to_excel(file, sheet_name='指标数据', index=False) - df.to_excel(file, sheet_name='指标列表', index=False) - - df_zhibiaoshuju = df1.copy() - df_zhibiaoliebiao = df.copy() - return df_zhibiaoshuju,df_zhibiaoliebiao - - def push_data(self,data): - - today = datetime.date.today().strftime("%Y-%m-%d") - - # 定义你的headers，这里可以包含多个参数 - self.headers = { - 'nonce': self.signature.nonce, # 例如，一个认证令牌 - 'timestamp': str(self.signature.timestamp), # 自定义的header参数 - 'appid': self.signature.APPID, # 另一个自定义的header参数 - 'signature': self.signature.signature - } - - # 发送post请求 上传数据 - logger.info('请求参数：',data) - response = requests.post(self.edbdatapushurl, headers=self.headers,data=json.dumps(data)) - - # 检查响应状态码 - if response.status_code == 200: - data = response.json() # 假设接口返回的是JSON数据 - - logger.info('上传成功，响应为：', data) - - else: - # 请求失败，打印错误信息 - logger.info(f'Error: {response.status_code}, {response.text}') - # 主动抛出异常 - raise Exception(f'Error: {response.status_code}, {response.text}') - - def del_zhibiao(self,IndexCodeList): - today = datetime.date.today().strftime("%Y-%m-%d") - - # 定义你的headers，这里可以包含多个参数 - self.headers = { - 'nonce': self.signature.nonce, # 例如，一个认证令牌 - 'timestamp': str(self.signature.timestamp), # 自定义的header参数 - 'appid': self.signature.APPID, # 另一个自定义的header参数 - 'signature': self.signature.signature - } - - data = { - "IndexCodeList": IndexCodeList #指标编码列表 - } - # 发送post请求 上传数据 - response = requests.post(self.edbdeleteurl, headers=self.headers,data=json.dumps(data)) - - - # 检查响应状态码 - if response.status_code == 200: - data = response.json() # 假设接口返回的是JSON数据 - - logger.info('删除成功，响应为：', data) - - else: - # 请求失败，打印错误信息 - logger.info(f'Error: {response.status_code}, {response.text}') - # 主动抛出异常 - raise Exception(f'Error: {response.status_code}, {response.text}') - - def del_business(self,data): - '''' - 接口地址 - https://console-docs.apipost.cn/preview/fce869601d0be1d9/9a637c2f9ed0c589?target_id=d3cafcbf-a68c-42b3-b105-7bbd0e95a9cd - - 请求体 body - { - "IndexCode": "W001067", //指标编码 - "StartDate": "2020-04-20", //指标需要删除的开始日期（>=），如果开始日期和结束日期相等，那么就是删除该日期 - "EndDate": "2024-05-28" //指标需要删除的结束日期（<=），如果开始日期和结束日期相等，那么就是删除该日期 - } - ''' - today = datetime.date.today().strftime("%Y-%m-%d") - - # 定义你的headers，这里可以包含多个参数 - self.headers = { - 'nonce': self.signature.nonce, # 例如，一个认证令牌 - 'timestamp': str(self.signature.timestamp), # 自定义的header参数 - 'appid': self.signature.APPID, # 另一个自定义的header参数 - 'signature': self.signature.signature - } - - - # 发送post请求 上传数据 - response = requests.post(self.edbbusinessurl, headers=self.headers,data=json.dumps(data)) - - - # 检查响应状态码 - if response.status_code == 200: - data = response.json() # 假设接口返回的是JSON数据 - - logger.info('删除成功，响应为：', data) - - else: - # 请求失败，打印错误信息 - logger.info(f'Error: {response.status_code}, {response.text}') - # 主动抛出异常 - raise Exception(f'Error: {response.status_code}, {response.text}') - - -def get_market_data(end_time,df): - """ - 获取市场数据，拼接到df中 - """ - # 获取token - token = get_head_auth_report() - # 定义请求参数 - query_data_list_item_nos_data['data']['dateEnd'] = end_time.replace('-','') - # 发送请求 - headers = {"Authorization": token} - logger.info('获取数据中...') - items_res = requests.post(url=query_data_list_item_nos_url, headers=headers, json=query_data_list_item_nos_data, timeout=(3, 35)) - json_data = json.loads(items_res.text) - logger.info(f"获取到的数据:{json_data}") - df3 = pd.DataFrame(json_data['data']) - # 按照dataItemNo 分组 得到多个dataframe ，最后根据dataDate merge 成一个dataframe - df2 = pd.DataFrame() - for i in df3['dataItemNo'].unique(): - df1 = df3[df3['dataItemNo'] == i] - df1 = df1[['dataDate', 'dataValue']] - df1 = df1.rename(columns={'dataValue': i}) - if len(df2) == 0: - df2 = df1 - continue - df2 = pd.merge(df2, df1, how='left') - df2 = df2.rename(columns={'dataDate': 'date'}) - # 20240101 转换为 2024-01-01 - df2['date'] = pd.to_datetime(df2['date'], format='%Y%m%d') - df2['date'] = df2['date'].dt.strftime('%Y-%m-%d') - df = pd.merge(df, df2, how='left',on='date') - return df - - -def get_high_low_data(df): - # 读取excel 从第五行开始 - df1 = pd.read_excel(os.path.join(dataset,'数据项下载.xls'),header=5, names=['numid','date', 'Brentzdj', 'Brentzgj']) - # 合并数据 - df = pd.merge(df, df1, how='left',on='date') - return df - - -# 时间特征，年，月，一年的多少天，周几，第几周，第几季度，每月的第几天， 每季度的第几天，是否每月的第一天，是否每月的最后一天，是否每季度的第一天，是否每季度的最后一天，是否每年的第一天，是否每年的最后一天 -def addtimecharacteristics(df,dataset): - """ - 为输入的 DataFrame 添加日期相关信息列 - - 参数： - df (pandas.DataFrame): 包含日期列 'ds' 的 DataFrame - - 返回： - pandas.DataFrame: 添加了相关列的 DataFrame - """ - df['year'] = df['ds'].dt.year - df['month'] = df['ds'].dt.month - df['day'] = df['ds'].dt.day - df['dayofweek'] = df['ds'].dt.dayofweek - df['weekofyear'] = df['ds'].dt.isocalendar().week - df['dayofyear'] = df['ds'].dt.dayofyear - df['quarternum'] = df['ds'].dt.quarter - # 将ds列转换为季度Period对象 - df['quarter'] = df['ds'].dt.to_period('Q') - # 获取每个季度的开始日期 - df['quarter_start'] = df['quarter'].dt.to_timestamp('s') - # 计算每个日期是所在季度的第几天 - df['dayofquarter'] = (df['ds'] - df['quarter_start']).dt.days + 1 - # 是否月初 - df['is_month_start'] = df['ds'].dt.is_month_start.astype(int) - # 是否月末 - df['is_month_end'] = df['ds'].dt.is_month_end.astype(int) - # 是否季度初 - df['is_quarter_start'] = df['ds'].dt.is_quarter_start.astype(int) - # 是否季度末 - df['is_quarter_end'] = df['ds'].dt.is_quarter_end.astype(int) - # 是否年初 - df['is_year_start'] = df['ds'].dt.is_year_start.astype(int) - # 是否年末 - df['is_year_end'] = df['ds'].dt.is_year_end.astype(int) - # 去掉 quarter_start quarter - df.drop(columns=['quarter_start','quarter'],inplace=True) - df.to_csv(os.path.join(dataset,'指标数据添加时间特征.csv'), index=False) - return df diff --git a/main_yongan.py b/main_yongan.py index 3a4807f..cf7a540 100644 --- a/main_yongan.py +++ b/main_yongan.py @@ -1,7 +1,7 @@ # 读取配置 -from lib.dataread_yongan import * +from lib.dataread import * from lib.tools import SendMail,exception_logger -from models.nerulforcastmodels import ex_Model,model_losss,model_losss_juxiting,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting +from models.nerulforcastmodels import ex_Model,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_yongan import glob import torch @@ -243,7 +243,7 @@ def predict_main(): # logger.info('模型训练完成') # logger.info('训练数据绘图ing') - # model_results3 = model_losss(sqlitedb,end_time=end_time) + # model_results3 = model_losss_yongan(sqlitedb,end_time=end_time,table_name_prefix=table_name_prefix) # logger.info('训练数据绘图end') # 模型报告 diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py index e14c5a7..3fdb937 100644 --- a/models/nerulforcastmodels.py +++ b/models/nerulforcastmodels.py @@ -226,6 +226,413 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien return +# 雍安环境预测评估指数 +@exception_logger +def model_losss_yongan(sqlitedb,end_time,table_name_prefix): + global dataset + global rote + most_model = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]] + most_model_name = most_model[0] + + # 预测数据处理 predict + # df_combined = loadcsv(os.path.join(dataset,"cross_validation.csv")) + # df_combined = dateConvert(df_combined) + df_combined = sqlitedb.select_data('accuracy',where_condition=f"created_dt <= '{end_time}'") + df_combined4 = df_combined.copy() # 备份df_combined,后面画图需要 + # 删除缺失值大于80%的列 + logger.info(df_combined.shape) + df_combined = df_combined.loc[:, df_combined.isnull().mean() < 0.8] + logger.info(df_combined.shape) + # 删除缺失值 + df_combined.dropna(inplace=True) + logger.info(df_combined.shape) + # 其他列转为数值类型 + df_combined = df_combined.astype({col: 'float32' for col in df_combined.columns if col not in ['CREAT_DATE','ds','created_dt'] }) + # 使用 groupby 和 transform 结合 lambda 函数来获取每个分组中 cutoff 的最小值，并创建一个新的列来存储这个最大值 + df_combined['max_cutoff'] = df_combined.groupby('ds')['CREAT_DATE'].transform('max') + + # 然后筛选出那些 cutoff 等于 max_cutoff 的行，这样就得到了每个分组中 cutoff 最大的行，并保留了其他列 + df_combined = df_combined[df_combined['CREAT_DATE'] == df_combined['max_cutoff']] + # 删除模型生成的cutoff列 + df_combined.drop(columns=['CREAT_DATE', 'max_cutoff','created_dt','min_within_quantile','max_within_quantile','id','min_price','max_price','LOW_PRICE','HIGH_PRICE','mean'], inplace=True) + # 获取模型名称 + modelnames = df_combined.columns.to_list()[1:] + if 'y' in modelnames: + modelnames.remove('y') + df_combined3 = df_combined.copy() # 备份df_combined,后面画图需要 + + + # 空的列表存储每个模型的MSE、RMSE、MAE、MAPE、SMAPE + cellText = [] + + # 遍历模型名称，计算模型评估指标 + for model in modelnames: + modelmse = mse(df_combined['y'], df_combined[model]) + modelrmse = rmse(df_combined['y'], df_combined[model]) + modelmae = mae(df_combined['y'], df_combined[model]) + # modelmape = mape(df_combined['y'], df_combined[model]) + # modelsmape = smape(df_combined['y'], df_combined[model]) + # modelr2 = r2_score(df_combined['y'], df_combined[model]) + cellText.append([model,round(modelmse, 3), round(modelrmse, 3), round(modelmae, 3)]) + + model_results3 = pd.DataFrame(cellText,columns=['模型(Model)','平均平方误差(MSE)', '均方根误差(RMSE)', '平均绝对误差(MAE)']) + # 按MSE降序排列 + model_results3 = model_results3.sort_values(by='平均平方误差(MSE)', ascending=True) + model_results3.to_csv(os.path.join(dataset,"model_evaluation.csv"),index=False) + modelnames = model_results3['模型(Model)'].tolist() + allmodelnames = modelnames.copy() + # 保存5个最佳模型的名称 + if len(modelnames) > 5: + modelnames = modelnames[0:5] + if is_fivemodels: + pass + else: + with open(os.path.join(dataset,"best_modelnames.txt"), 'w') as f: + f.write(','.join(modelnames) + '\n') + + # 预测值与真实值对比图 + plt.rcParams['font.sans-serif'] = ['SimHei'] + plt.figure(figsize=(15, 10)) + for n,model in enumerate(modelnames[:5]): + plt.subplot(3, 2, n+1) + plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值') + plt.plot(df_combined3['ds'], df_combined3[model], label=model) + plt.legend() + plt.xlabel('日期') + plt.ylabel('价格') + plt.title(model+'拟合') + plt.subplots_adjust(hspace=0.5) + plt.savefig(os.path.join(dataset,'预测值与真实值对比图.png'), bbox_inches='tight') + plt.close() + + + # # 历史数据+预测数据 + # # 拼接未来时间预测 + df_predict = pd.read_csv(os.path.join(dataset,'predict.csv')) + df_predict.drop('unique_id',inplace=True,axis=1) + df_predict.dropna(axis=1,inplace=True) + + try: + df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y-%m-%d') + except ValueError : + df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d') + + def first_row_to_database(df): + # # 取第一行数据存储到数据库中 + first_row = df.head(1) + first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00') + # 将预测结果保存到数据库 + if not sqlitedb.check_table_exists('trueandpredict'): + first_row.to_sql('trueandpredict',sqlitedb.connection,index=False) + else: + for col in first_row.columns: + sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT') + for row in first_row.itertuples(index=False): + row_dict = row._asdict() + columns=row_dict.keys() + check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'") + if len(check_query) > 0: + set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()]) + sqlitedb.update_data('trueandpredict',set_clause,where_condition = f"ds = '{row.ds}'") + continue + sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns) + + first_row_to_database(df_predict) + + df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True) + + # 计算每个模型与最佳模型的绝对误差比例，根据设置的阈值rote筛选预测值显示最大最小值 + names = [] + names_df = df_combined3.copy() + for col in allmodelnames: + names_df[f'{col}-{most_model_name}-误差比例'] = abs(names_df[col] - names_df[most_model_name]) / names_df[most_model_name] + names.append(f'{col}-{most_model_name}-误差比例') + + names_df = names_df[names] + def add_rote_column(row): + columns = [] + for r in names_df.columns: + if row[r] <= rote: + columns.append(r.split('-')[0]) + return pd.Series([columns], index=['columns']) + names_df['columns'] = names_df.apply(add_rote_column, axis=1) + + def add_upper_lower_bound(row): + print(row['columns']) + print(type(row['columns'])) + # 计算上边界值 + upper_bound = df_combined3.loc[row.name,row['columns']].max() + # 计算下边界值 + lower_bound = df_combined3.loc[row.name,row['columns']].min() + return pd.Series([lower_bound, upper_bound], index=['min_within_quantile', 'max_within_quantile']) + df_combined3[['min_within_quantile','max_within_quantile']] = names_df.apply(add_upper_lower_bound, axis=1) + + def find_closest_values(row): + x = row.y + if x is None or np.isnan(x): + return pd.Series([None, None], index=['min_price','max_price']) + # row = row.drop('ds') + row = row.values.tolist() + row.sort() + print(row) + # x 在row中的索引 + index = row.index(x) + if index == 0: + return pd.Series([row[index+1], row[index+2]], index=['min_price','max_price']) + elif index == len(row)-1: + return pd.Series([row[index-2], row[index-1]], index=['min_price','max_price']) + else: + return pd.Series([row[index-1], row[index+1]], index=['min_price','max_price']) + + def find_most_common_model(): + # 最多频率的模型名称 + min_model_max_frequency_model = df_combined3['min_model'].tail(60).value_counts().idxmax() + max_model_max_frequency_model = df_combined3['max_model'].tail(60).value_counts().idxmax() + if min_model_max_frequency_model == max_model_max_frequency_model: + # 取60天第二多的模型 + max_model_max_frequency_model = df_combined3['max_model'].tail(60).value_counts().nlargest(2).index[1] + + df_predict['min_model'] = min_model_max_frequency_model + df_predict['max_model'] = max_model_max_frequency_model + df_predict['min_within_quantile'] = df_predict[min_model_max_frequency_model] + df_predict['max_within_quantile'] = df_predict[max_model_max_frequency_model] + + + # find_most_common_model() + + df_combined3['ds'] = pd.to_datetime(df_combined3['ds']) + df_combined3['ds'] = df_combined3['ds'].dt.strftime('%Y-%m-%d') + df_predict2 = df_combined3.tail(horizon) + + # 保存到数据库 + if not sqlitedb.check_table_exists(f'{table_name_prefix}accuracy'): + columns = ','.join(df_combined3.columns.to_list()+['id','CREAT_DATE','min_price','max_price','LOW_PRICE','HIGH_PRICE','mean']) + sqlitedb.create_table('accuracy',columns=columns) + existing_data = sqlitedb.select_data(table_name = "accuracy") + + if not existing_data.empty: + max_id = existing_data['id'].astype(int).max() + df_predict2['id'] = range(max_id + 1, max_id + 1 + len(df_predict2)) + else: + df_predict2['id'] = range(1, 1 + len(df_predict2)) + df_predict2['CREAT_DATE'] = end_time + + save_to_database(sqlitedb,df_predict2,"accuracy",end_time) + + # 上周准确率计算 + accuracy_df = sqlitedb.select_data(table_name = "accuracy") + predict_y = accuracy_df.copy() + # ids = predict_y[predict_y['min_price'].isnull()]['id'].tolist() + ids = predict_y['id'].tolist() + # 准确率基准与绘图上下界逻辑一致 + # predict_y[['min_price','max_price']] = predict_y[['min_within_quantile','max_within_quantile']] + # 模型评估前五均值 + # predict_y['min_price'] = predict_y[modelnames].mean(axis=1) -1 + # predict_y['max_price'] = predict_y[modelnames].mean(axis=1) +1 + # 模型评估前十均值 + predict_y['min_price'] = predict_y[allmodelnames[0:10]].mean(axis=1) -1.5 + predict_y['mean'] = predict_y[allmodelnames[0:10]].mean(axis=1) + predict_y['max_price'] = predict_y[allmodelnames[0:10]].mean(axis=1) +1.5 + # 模型评估前十最大最小 + # allmodelnames 和 predict_y 列 重复的 + # allmodelnames = [col for col in allmodelnames if col in predict_y.columns] + # predict_y['min_price'] = predict_y[allmodelnames[0:10]].min(axis=1) + # predict_y['max_price'] = predict_y[allmodelnames[0:10]].max(axis=1) + for id in ids: + row = predict_y[predict_y['id'] == id] + try: + sqlitedb.update_data('accuracy',f"min_price = {row['min_price'].values[0]},max_price = {row['max_price'].values[0]},mean={row['mean'].values[0]}",f"id = {id}") + except: + logger.error(f'更新accuracy表中的min_price,max_price,mean值失败，row={row}') + + df = accuracy_df.copy() + df['ds'] = pd.to_datetime(df['ds']) + df = df.reindex() + + # 判断预测值在不在布伦特最高最低价范围内，准确率为1，否则为0 + def is_within_range(row): + for model in allmodelnames: + if row['LOW_PRICE'] <= row[col] <= row['HIGH_PRICE']: + return 1 + else: + return 0 + + # 定义一个函数来计算准确率 + def calculate_accuracy(row): + # 比较真实最高最低，和预测最高最低 计算准确率 + # 全子集情况： + if (row['max_price'] >= row['HIGH_PRICE'] and row['min_price'] <= row['LOW_PRICE']) or \ + (row['max_price'] <= row['HIGH_PRICE'] and row['min_price'] >= row['LOW_PRICE']): + return 1 + # 无交集情况： + if row['max_price'] < row['LOW_PRICE'] or \ + row['min_price'] > row['HIGH_PRICE']: + return 0 + # 有交集情况： + else: + sorted_prices = sorted([row['LOW_PRICE'], row['min_price'], row['max_price'], row['HIGH_PRICE']]) + middle_diff = sorted_prices[2] - sorted_prices[1] + price_range = row['HIGH_PRICE'] - row['LOW_PRICE'] + accuracy = middle_diff / price_range + return accuracy + + columns = ['HIGH_PRICE','LOW_PRICE','min_price','max_price'] + df[columns] = df[columns].astype(float) + df['ACCURACY'] = df.apply(calculate_accuracy, axis=1) + # df['ACCURACY'] = df.apply(is_within_range, axis=1) + + # 计算准确率并保存结果 + def _get_accuracy_rate(df,create_dates,ds_dates,endtime): + df3 = df.copy() + df3 = df3[df3['CREAT_DATE'].isin(create_dates)] + df3 = df3[df3['ds'].isin(ds_dates)] + accuracy_rote = 0 + for i,group in df3.groupby('CREAT_DATE'): + accuracy_rote += (group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1] + accuracy_rote = round(accuracy_rote,2) + df4 = pd.DataFrame(columns=['开始日期','结束日期','准确率']) + df4.loc[len(df4)] = {'开始日期':ds_dates[0],'结束日期':ds_dates[-1],'准确率':accuracy_rote} + df4.to_sql("accuracy_rote", con=sqlitedb.connection, if_exists='append', index=False) + create_dates,ds_dates = get_week_date(end_time) + _get_accuracy_rate(df,create_dates,ds_dates,end_time) + + def _add_abs_error_rate(): + # 计算每个预测值与真实值之间的偏差率 + for model in allmodelnames: + df_combined3[f'{model}_abs_error_rate'] = abs(df_combined3['y'] - df_combined3[model]) / df_combined3['y'] + + # 获取每行对应的最小偏差率值 + min_abs_error_rate_values = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].min(), axis=1) + # 获取每行对应的最小偏差率值对应的列名 + min_abs_error_rate_column_name = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].idxmin(), axis=1) + # 将列名索引转换为列名 + min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0]) + # 获取最小偏差率对应的模型的预测值 + min_abs_error_rate_predictions = df_combined3.apply(lambda row: row[min_abs_error_rate_column_name[row.name]], axis=1) + # 将最小偏差率对应的模型的预测值添加到DataFrame中 + df_combined3['min_abs_error_rate_prediction'] = min_abs_error_rate_predictions + df_combined3['min_abs_error_rate_column_name'] = min_abs_error_rate_column_name + # _add_abs_error_rate() + + # 判断 df 的数值列转为float + for col in df_combined3.columns: + try: + if col != 'ds': + df_combined3[col] = df_combined3[col].astype(float) + df_combined3[col] = df_combined3[col].round(2) + except ValueError: + pass + df_combined3.to_csv(os.path.join(dataset,"testandpredict_groupby.csv"),index=False) + + + # 历史价格+预测价格 + sqlitedb.drop_table('testandpredict_groupby') + df_combined3.to_sql('testandpredict_groupby',sqlitedb.connection,index=False) + + def _plt_predict_ture(df): + lens = df.shape[0] if df.shape[0] < 180 else 90 + df = df[-lens:] # 取180个数据点画图 + # 历史价格 + plt.figure(figsize=(20, 10)) + plt.plot(df['ds'], df['y'], label='真实值') + # 颜色填充 + plt.fill_between(df['ds'], df['max_within_quantile'], df['min_within_quantile'], alpha=0.2) + markers = ['o', 's', '^', 'D', 'v', '*', 'p', 'h', 'H', '+', 'x', 'd'] + random_marker = random.choice(markers) + for model in modelnames: + plt.plot(df['ds'][-horizon:], df[model][-horizon:], label=model,marker=random_marker) + # plt.plot(df_combined3['ds'], df_combined3['min_abs_error_rate_prediction'], label='最小绝对误差', linestyle='--', color='orange') + # 网格 + plt.grid(True) + # 显示历史值 + for i, j in zip(df['ds'], df['y']): + plt.text(i, j, str(j), ha='center', va='bottom') + + # 当前日期画竖虚线 + plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--') + plt.legend() + plt.xlabel('日期') + plt.ylabel('价格') + + plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight') + plt.close() + + + def _plt_modeltopten_predict_ture(df): + df['max_cutoff'] = df.groupby('ds')['CREAT_DATE'].transform('max') + df = df[df['CREAT_DATE'] == df['max_cutoff']] + df['mean'] = df['mean'].astype(float) + lens = df.shape[0] if df.shape[0] < 180 else 180 + df = df[-lens:] # 取180个数据点画图 + # 历史价格 + plt.figure(figsize=(20, 10)) + plt.plot(df['ds'], df['y'], label='真实值') + plt.plot(df['ds'], df['mean'], label='模型前十均值', linestyle='--', color='orange') + # 颜色填充 + plt.fill_between(df['ds'], df['max_price'], df['min_price'], alpha=0.2) + # markers = ['o', 's', '^', 'D', 'v', '*', 'p', 'h', 'H', '+', 'x', 'd'] + # random_marker = random.choice(markers) + # for model in allmodelnames: + # for model in ['BiTCN','RNN']: + # plt.plot(df['ds'], df[model], label=model,marker=random_marker) + # plt.plot(df_combined3['ds'], df_combined3['min_abs_error_rate_prediction'], label='最小绝对误差', linestyle='--', color='orange') + # 网格 + plt.grid(True) + # 显示历史值 + for i, j in zip(df['ds'], df['y']): + plt.text(i, j, str(j), ha='center', va='bottom') + + # 当前日期画竖虚线 + plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--') + plt.legend() + plt.xlabel('日期') + plt.ylabel('价格') + + plt.savefig(os.path.join(dataset,'历史价格-预测值1.png'), bbox_inches='tight') + plt.close() + + + def _plt_predict_table(df): + # 预测值表格 + fig, ax = plt.subplots(figsize=(20, 6)) + ax.axis('off') # 关闭坐标轴 + # 数值保留2位小数 + df = df.round(2) + df = df[-horizon:] + df['Day'] = [f'Day_{i}' for i in range(1,horizon+1)] + # Day列放到最前面 + df = df[['Day'] + list(df.columns[:-1])] + table = ax.table(cellText=df.values, colLabels=df.columns, loc='center') + #加宽表格 + table.auto_set_font_size(False) + table.set_fontsize(10) + + # 设置表格样式，列数据最小的用绿色标识 + plt.savefig(os.path.join(dataset,'预测值表格.png'), bbox_inches='tight') + plt.close() + + def _plt_model_results3(): + # 可视化评估结果 + plt.rcParams['font.sans-serif'] = ['SimHei'] + fig, ax = plt.subplots(figsize=(20, 10)) + ax.axis('off') # 关闭坐标轴 + table = ax.table(cellText=model_results3.values, colLabels=model_results3.columns, loc='center') + # 加宽表格 + table.auto_set_font_size(False) + table.set_fontsize(10) + + # 设置表格样式，列数据最小的用绿色标识 + plt.savefig(os.path.join(dataset,'模型评估.png'), bbox_inches='tight') + plt.close() + + _plt_predict_ture(df_combined3) + _plt_modeltopten_predict_ture(df_combined4) + _plt_predict_table(df_combined3) + _plt_model_results3() + + return model_results3 + + # 原油计算预测评估指数 @exception_logger def model_losss(sqlitedb,end_time): diff --git a/yongandataset/jbsh_yuanyou.db b/yongandataset/jbsh_yuanyou.db index e82da782a678ab0fca2480f4d1281365d05b73a6..b35651fca944bcfa371e03af9fd43a227837d5c9 100644 GIT binary patch delta 793 zcmZozpxdxOcY?HF0s{lXRv_*MVk-s)#!C}*j2RO)Cal(HOxet1@S~qOjp@r|_5;V+ zm@Syan7(W_R8U}IQb?OzcUY2h-;Ti7t#UgQQc-v*D7<7KPqDf7aC_}xMj&PaV&?6& 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