原油周度预测，配置参数导入逻辑优化
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workpc
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lib/dataread.py
407
lib/dataread.py
@ -1,6 +1,5 @@
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# 导入模块
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from config_jingbo_zhoudu import *
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from reportlab.lib.units import cm # 单位：cm
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from reportlab.graphics.shapes import Drawing # 绘图工具
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from reportlab.graphics.charts.legends import Legend # 图例类
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@ -51,8 +50,97 @@ plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
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# from config_jingbo import logger
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global_config = {
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# 核心配置项
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'logger': None, # 日志记录器
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'dataset': None, # 数据集路径
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'y': None, # 目标变量列名
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'is_fivemodels': None,
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# 模型参数
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'data_set': None, # 数据集名称
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'input_size': None, # 输入维度
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'horizon': None, # 预测步长
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'train_steps': None, # 训练步数
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'val_check_steps': None, # 验证间隔
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# 特征工程开关
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'is_del_corr': None, # 是否删除相关性特征
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'is_del_tow_month': None, # 是否删除近两月未更新特征
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'is_eta': None, # ETA功能开关
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'is_update_eta': None, # 更新ETA开关
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'is_update_eta_data': None, # ETA数据更新开关
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'early_stop_patience_steps': None, # 早停步数
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'is_update_report': None, # 是否更新报告开关
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# 时间参数
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'start_year': None, # 起始年份
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'end_time': None, # 新增结束时间参数 ← 增加缺失的配置项
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'freq': [None], # 数据频率（保留列表结构）
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# 数据上传
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'upload_url': None, # 主数据上传地址
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'upload_headers': None, # 上传请求头
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'upload_warning_url': None, # 预警数据上传地址
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'upload_warning_data': None, # 预警数据结构
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# 查询接口
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'query_data_list_item_nos_url': None, # 数据项查询地址
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'query_data_list_item_nos_data': None, # 数据项查询参数
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# 字段映射
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'offsite_col': None, # 站点字段
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'avg_col': None, # 平均值字段
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'offsite': None, # 站点名称
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'edbcodenamedict': None, # EDB编码映射
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'rote': None, # 绘图上下界阈值
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# 接口配置（原有配置）
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'login_pushreport_url': None,
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'login_data': None,
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'upload_warning_headers': None,
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# ETA配置
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'APPID': None,
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'SECRET': None,
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# 数据库配置
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'sqlitedb': None,
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}
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# logger = global_config['logger']
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# dataset = global_config['dataset']
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# y = global_config['y']
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# data_set = global_config['data_set']
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# input_size = global_config['input_size']
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# horizon = global_config['horizon']
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# train_steps = global_config['train_steps']
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# val_check_steps = global_config['val_check_steps']
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# is_del_corr = global_config['is_del_corr']
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# is_del_tow_month = global_config['is_del_tow_month']
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# is_eta = global_config['is_eta']
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# is_update_eta = global_config['is_update_eta']
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# is_update_eta_data = global_config['is_update_eta_data']
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# start_year = global_config['start_year']
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# end_time = global_config['end_time']
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# freq = global_config['freq'][0]
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# offsite_col = global_config['offsite_col']
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# avg_cols = global_config['avg_col']
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# offsite = global_config['offsite']
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# edbcodenamedict = global_config['edbcodenamedict']
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# query_data_list_item_nos_url = global_config['query_data_list_item_nos_url']
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# query_data_list_item_nos_data = global_config['query_data_list_item_nos_data']
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# config.login_pushreport_url = global_config['config.login_pushreport_url']
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# login_data = global_config['login_data']
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# upload_url = global_config['upload_url']
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# upload_warning_url = global_config['upload_warning_url']
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# upload_warning_data = global_config['upload_warning_data']
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# warning_data = global_config['upload_warning_data']
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# APPID = global_config['APPID']
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# SECRET = global_config['SECRET']
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# 定义函数
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def loadcsv(filename):
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"""
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读取指定文件名的 CSV 文件。
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@ -145,15 +233,16 @@ def get_head_auth_report():
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返回:
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str: 如果登录成功，返回认证令牌；否则返回 None。
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"""
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logger.info("获取token中...")
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logger.info(f'url:{login_pushreport_url},login_data:{login_data}')
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config.logger.info("获取token中...")
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config.logger.info(
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f'url:{config.login_pushreport_url},login_data:{config.login_data}')
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# 发送 POST 请求到登录 URL，携带登录数据
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login_res = requests.post(url=login_pushreport_url,
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json=login_data, timeout=(3, 30))
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login_res = requests.post(url=config.login_pushreport_url,
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json=config.login_data, timeout=(3, 30))
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# 将响应内容转换为 JSON 格式
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text = json.loads(login_res.text)
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logger.info(f'token接口响应：{text}')
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config.logger.info(f'token接口响应：{text}')
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# 如果响应状态为成功
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if text["status"]:
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# 从响应数据中获取认证令牌
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@ -180,30 +269,30 @@ def upload_report_data(token, upload_data):
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headers = {"Authorization": token}
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# 打印日志，显示正在上传报告数据
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logger.info("报告上传中...")
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config.logger.info("报告上传中...")
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# 打印日志，显示认证头部信息
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logger.info(f"token:{token}")
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config.logger.info(f"token:{token}")
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# 打印日志，显示要上传的报告数据
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logger.info(f"upload_data:{upload_data}")
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config.logger.info(f"upload_data:{upload_data}")
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# 发送POST请求，上传报告数据
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upload_res = requests.post(
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url=upload_url, headers=headers, json=upload_data, timeout=(3, 15))
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url=config.upload_url, headers=headers, json=upload_data, timeout=(3, 15))
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# 将响应内容转换为 JSON 格式
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upload_res = json.loads(upload_res.text)
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# 打印日志，显示响应内容
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logger.info(upload_res)
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config.logger.info(upload_res)
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# 如果上传成功，返回响应对象
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if upload_res:
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return upload_res
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# 如果上传失败，打印日志并返回None
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else:
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logger.info("报告上传失败")
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config.logger.info("报告上传失败")
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return None
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@ -224,27 +313,27 @@ def upload_warning_data(warning_data):
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headers = {"Authorization": token}
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# 打印日志，显示正在上传预警数据
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logger.info("预警上传中...")
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config.logger.info("预警上传中...")
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# 打印日志，显示上传的URL
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logger.info(f"upload_warning_url:{upload_warning_url}")
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config.logger.info(f"upload_warning_url:{config.upload_warning_url}")
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# 打印日志，显示认证头部信息
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logger.info(f"token:{token}")
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config.logger.info(f"token:{token}")
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# 打印日志，显示要上传的预警数据
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logger.info(f"warning_data:{warning_data}")
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config.logger.info(f"warning_data:{config.warning_data}")
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# 发送POST请求，上传预警数据
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upload_res = requests.post(
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url=upload_warning_url, headers=headers, json=warning_data, timeout=(3, 15))
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url=config.upload_warning_url, headers=headers, json=config.warning_data, timeout=(3, 15))
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# 如果上传成功，返回响应对象
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if upload_res:
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return upload_res
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# 如果上传失败，打印日志并返回None
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else:
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logger.info("预警上传失败")
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config.logger.info("预警上传失败")
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return None
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@ -259,7 +348,7 @@ def upload_warning_info(df_count):
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None
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"""
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# 打印日志，显示正在上传预警信息
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logger.info(f'上传预警信息')
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config.logger.info(f'上传预警信息')
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try:
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# 获取当前日期
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@ -270,17 +359,17 @@ def upload_warning_info(df_count):
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content = f'{warning_date}有{df_count}个停更'
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# 更新预警数据中的日期和内容
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warning_data['data']['WARNING_DATE'] = warning_date2
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warning_data['data']['WARNING_CONTENT'] = content
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config.warning_data['data']['WARNING_DATE'] = warning_date2
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config.warning_data['data']['WARNING_CONTENT'] = content
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# 调用 upload_warning_data 函数上传预警数据
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upload_warning_data(warning_data)
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upload_warning_data(config.warning_data)
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# 打印日志，显示上传预警信息成功
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logger.info(f'上传预警信息成功')
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config.logger.info(f'上传预警信息成功')
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except Exception as e:
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# 打印日志，显示上传预警信息失败，并记录异常信息
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logger.error(f'上传预警信息失败：{e}')
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config.logger.error(f'上传预警信息失败：{e}')
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def create_feature_last_update_time(df):
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@ -317,7 +406,8 @@ def create_feature_last_update_time(df):
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0]).total_seconds() / 3600 / 24
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last_update_time_datetime = datetime.datetime.strptime(
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last_update_time, '%Y-%m-%d')
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last_update_date = end_time if end_time != '' else datetime.datetime.now().strftime('%Y-%m-%d')
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last_update_date = config.end_time if config.end_time != '' else datetime.datetime.now(
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).strftime('%Y-%m-%d')
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end_time_datetime = datetime.datetime.strptime(
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last_update_date, '%Y-%m-%d')
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early_warning_date = last_update_time_datetime + \
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@ -327,18 +417,19 @@ def create_feature_last_update_time(df):
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early_warning_date = early_warning_date.strftime('%Y-%m-%d')
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except KeyError:
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time_diff = 0
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early_warning_date = end_time
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early_warning_date = config.end_time
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continue
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values = values + [time_diff, early_warning_date, stop_update_period]
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last_update_times_df.loc[len(last_update_times_df)] = values
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logger.info(f"Column {column} was last updated at {last_update_time}")
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config.logger.info(
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f"Column {column} was last updated at {last_update_time}")
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y_last_update_time = last_update_times_df[last_update_times_df['feature']
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== 'y']['warning_date'].values[0]
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last_update_times_df.to_csv(os.path.join(
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dataset, 'last_update_times.csv'), index=False)
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logger.info('特征停更信息保存到文件：last_update_times.csv')
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config.dataset, 'last_update_times.csv'), index=False)
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config.logger.info('特征停更信息保存到文件：last_update_times.csv')
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return last_update_times_df, y_last_update_time
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@ -378,7 +469,7 @@ def featurePindu(dataset):
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try:
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count = max(set(count), key=count.count)
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except ValueError:
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logger.info(f'{column}列数据为空')
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config.logger.info(f'{column}列数据为空')
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continue
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# 存储到字典中
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count_dict[column] = count
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@ -402,7 +493,7 @@ def featurePindu(dataset):
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# nan替换为 ' '
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pindu_dfs = pindu_dfs.fillna('')
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pindu_dfs.to_csv(os.path.join(dataset, '特征频度统计.csv'), index=False)
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logger.info(pindu_dfs)
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config.logger.info(pindu_dfs)
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featureInfo = f'特征信息：总共有{len(columns)-2}个'
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for i in pindu_dfs.columns:
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featureInfo += f',{i}'
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@ -419,10 +510,10 @@ def featurePindu(dataset):
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-- 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
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数据特征相关性分析：
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'''
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logger.info(featureInfo)
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config.logger.info(featureInfo)
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with open(os.path.join(dataset, '特征频度统计.txt'), 'w', encoding='utf-8') as f:
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f.write(featureInfo)
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logger.info('*'*200)
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config.logger.info('*'*200)
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def featureAnalysis(df, dataset, y):
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@ -477,10 +568,10 @@ def corr_feature(df):
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df_test_noscaler = df_test.copy() # 滞后处理备份
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df_noscaler = df_test.copy()
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# 画出相关性热力图
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df_test.to_csv(os.path.join(dataset, '同步相关性.csv'))
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df_test.to_csv(os.path.join(config.dataset, '同步相关性.csv'))
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corr = df_test.corr()
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# 保存相关系数
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corr.to_csv(os.path.join(dataset, '同步相关性系数.csv'))
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corr.to_csv(os.path.join(config.dataset, '同步相关性系数.csv'))
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# plt.figure(figsize=(10, 10))
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# sns.heatmap(corr, annot=True, cmap='coolwarm')
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# plt.savefig('dataset/同步相关性热力图.png')
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@ -502,7 +593,7 @@ def corr_feature(df):
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== col]['滞后周期'].values[0]
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# 滞后处理
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df[col] = df[col].shift(period)
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df.to_csv(os.path.join(dataset, '滞后处理后的数据集.csv'))
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df.to_csv(os.path.join(config.dataset, '滞后处理后的数据集.csv'))
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# corr_feture_noscaler = {} # 保存相关性最大的周期
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# 遍历df_test的每一列，计算相关性
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@ -510,7 +601,7 @@ def corr_feature(df):
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# # 跳过y列
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# if col in ['y']:
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# continue
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# logger.info('特征：', col)
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# config.logger.info('特征：', col)
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# # 特征滞后n个周期，计算与y的相关性
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# corr_dict = {}
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# try:
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@ -521,10 +612,10 @@ def corr_feature(df):
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# df_noscaler[col+'_'+str(i)] = df_noscaler[col].shift(i)
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# corr_dict[col+'_'+str(i)] = abs(df_noscaler[col+'_'+str(i)].corr(df_noscaler['y']))
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# except :
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# logger.info('特征：', col, '滑动错误，请查看')
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# config.logger.info('特征：', col, '滑动错误，请查看')
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# continue
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# 输出相关性最大的特征
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# logger.info(max(corr_dict, key=corr_dict.get), corr_dict[max(corr_dict, key=corr_dict.get)])
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# config.logger.info(max(corr_dict, key=corr_dict.get), corr_dict[max(corr_dict, key=corr_dict.get)])
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# corr_feture_noscaler[col] = max(corr_dict, key=corr_dict.get).split('_')[-1]
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# 画出最相关性最大的特征和y的折线图
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# plt.figure(figsize=(10, 5))
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@ -541,7 +632,7 @@ def corr_feature(df):
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# plt.savefig('dataset/特征与y的折线图_'+max(corr_dict, key=corr_dict.get).replace(':','_').replace('/','_').replace('(','_').replace(')','_')+'.png')
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# plt.close()
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# 结果保存到txt文件
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# logger.info('不参与标准化的特征滞后相关性写入txt文件')
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# config.logger.info('不参与标准化的特征滞后相关性写入txt文件')
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# with open('dataset/不参与标准化的特征滞后相关性.txt', 'w') as f:
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# for key, value in corr_feture_noscaler.items():
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# f.write('%s:%s\n' % (key, value))
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@ -576,7 +667,7 @@ def corr_feature(df):
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# # 跳过y列
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# if col == 'y':
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# continue
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# logger.info('特征：', col)
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# config.logger.info('特征：', col)
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# # 特征滞后n个周期，计算与y的相关性
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# corr_dict = {}
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# try:
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@ -587,10 +678,10 @@ def corr_feature(df):
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# 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')
|
||||
# config.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)])
|
||||
# config.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
|
||||
@ -684,8 +775,8 @@ def check_column(df, col_name, two_months_ago):
|
||||
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:
|
||||
if config.is_del_corr > 0:
|
||||
if abs(df_check_column[col_name].corr(df_check_column['y'])) < config.is_del_corr:
|
||||
print(f'相关系数小于0.6：{col_name}')
|
||||
return True
|
||||
|
||||
@ -710,50 +801,50 @@ def datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_time='', y
|
||||
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'].dt.year >= config.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}')
|
||||
# config.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}')
|
||||
# config.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:
|
||||
if config.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}')
|
||||
config.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}')
|
||||
config.logger.info(f'删除两月不更新特征后数据量：{df.shape}')
|
||||
|
||||
# 衍生时间特征
|
||||
if is_timefurture:
|
||||
df = addtimecharacteristics(df=df, dataset=dataset)
|
||||
|
||||
if freq == 'WW':
|
||||
if config.freq == 'WW':
|
||||
# 自定义周数据
|
||||
# 按weekofmothe分组取均值得到新的数据
|
||||
df = df.groupby(df['yearmonthweeks']).mean()
|
||||
# 时间列转换为日期格式字符串
|
||||
df['ds'] = df['ds'].dt.strftime('%Y-%m-%d')
|
||||
elif freq == 'W':
|
||||
elif config.freq == 'W':
|
||||
# 按周取样
|
||||
df = df.resample('W', on='ds').mean().reset_index()
|
||||
elif freq == 'M':
|
||||
elif config.freq == 'M':
|
||||
# 按月取样
|
||||
df = df.resample('M', on='ds').mean().reset_index()
|
||||
# 删除预测列空值的行
|
||||
''' 工作日缺失，如果删除，会影响预测结果，导致统计准确率出错 '''
|
||||
# df = df.dropna(subset=['y'])
|
||||
logger.info(f'删除预测列为空值的行后数据量：{df.shape}')
|
||||
config.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}')
|
||||
config.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(
|
||||
@ -797,40 +888,40 @@ def zhoududatachuli(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_time
|
||||
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'].dt.year >= config.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}')
|
||||
# config.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}')
|
||||
# config.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:
|
||||
if config.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}')
|
||||
config.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}')
|
||||
config.logger.info(f'删除两月不更新特征后数据量：{df.shape}')
|
||||
|
||||
if freq == 'W':
|
||||
if config.freq == 'W':
|
||||
# 按周取样
|
||||
df = df.resample('W', on='ds').mean().reset_index()
|
||||
elif freq == 'M':
|
||||
elif config.freq == 'M':
|
||||
# 按月取样
|
||||
df = df.resample('M', on='ds').mean().reset_index()
|
||||
# 删除预测列空值的行
|
||||
''' 工作日缺失，如果删除，会影响预测结果，导致统计准确率出错 '''
|
||||
# df = df.dropna(subset=['y'])
|
||||
logger.info(f'删除预测列为空值的行后数据量：{df.shape}')
|
||||
config.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}')
|
||||
config.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(
|
||||
@ -872,11 +963,11 @@ def datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_t
|
||||
df.rename(columns={datecol: 'ds'}, inplace=True)
|
||||
|
||||
# 指定列统一减少数值
|
||||
df[offsite_col] = df[offsite_col]-offsite
|
||||
df[config.offsite_col] = df[config.offsite_col]-config.offsite
|
||||
# 预测列为avg_cols的均值
|
||||
df[y] = df[avg_cols].mean(axis=1)
|
||||
df[y] = df[config.avg_cols].mean(axis=1)
|
||||
# 去掉多余的列avg_cols
|
||||
df = df.drop(columns=avg_cols)
|
||||
df = df.drop(columns=config.avg_cols)
|
||||
|
||||
# 重命名预测列
|
||||
df.rename(columns={y: 'y'}, inplace=True)
|
||||
@ -887,7 +978,7 @@ def datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_t
|
||||
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}')
|
||||
config.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)
|
||||
@ -907,13 +998,13 @@ def datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_t
|
||||
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}')
|
||||
config.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}')
|
||||
config.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}')
|
||||
config.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(
|
||||
@ -942,7 +1033,7 @@ def datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_t
|
||||
|
||||
|
||||
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)
|
||||
config.logger.info('getdata接收：'+filename+' '+datecol+' '+end_time)
|
||||
# 判断后缀名 csv或excel
|
||||
if filename.endswith('.csv'):
|
||||
df = loadcsv(filename)
|
||||
@ -959,7 +1050,7 @@ def getdata(filename, datecol='date', y='y', dataset='', add_kdj=False, is_timef
|
||||
|
||||
|
||||
def getzhoududata(filename, datecol='date', y='y', dataset='', add_kdj=False, is_timefurture=False, end_time=''):
|
||||
logger.info('getdata接收：'+filename+' '+datecol+' '+end_time)
|
||||
config.logger.info('getdata接收：'+filename+' '+datecol+' '+end_time)
|
||||
# 判断后缀名 csv或excel
|
||||
if filename.endswith('.csv'):
|
||||
df = loadcsv(filename)
|
||||
@ -976,7 +1067,7 @@ def getzhoududata(filename, datecol='date', y='y', dataset='', add_kdj=False, is
|
||||
|
||||
|
||||
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)
|
||||
config.logger.info('getdata接收：'+filename+' '+datecol+' '+end_time)
|
||||
# 判断后缀名 csv或excel
|
||||
if filename.endswith('.csv'):
|
||||
df = loadcsv(filename)
|
||||
@ -1002,6 +1093,103 @@ def sanitize_filename(filename):
|
||||
return sanitized
|
||||
|
||||
|
||||
class Config:
|
||||
# 核心配置
|
||||
@property
|
||||
def logger(self): return global_config['logger']
|
||||
@property
|
||||
def dataset(self): return global_config['dataset']
|
||||
@property
|
||||
def y(self): return global_config['y']
|
||||
@property
|
||||
def is_fivemodels(self): return global_config['is_fivemodels']
|
||||
|
||||
# 模型参数
|
||||
@property
|
||||
def data_set(self): return global_config['data_set']
|
||||
@property
|
||||
def input_size(self): return global_config['input_size']
|
||||
@property
|
||||
def horizon(self): return global_config['horizon']
|
||||
@property
|
||||
def train_steps(self): return global_config['train_steps']
|
||||
@property
|
||||
def val_check_steps(self): return global_config['val_check_steps']
|
||||
@property
|
||||
def rote(self): return global_config['rote']
|
||||
|
||||
# 特征工程开关
|
||||
@property
|
||||
def is_del_corr(self): return global_config['is_del_corr']
|
||||
@property
|
||||
def is_del_tow_month(self): return global_config['is_del_tow_month']
|
||||
@property
|
||||
def is_eta(self): return global_config['is_eta']
|
||||
@property
|
||||
def is_update_eta(self): return global_config['is_update_eta']
|
||||
@property
|
||||
def is_update_eta_data(self): return global_config['is_update_eta_data']
|
||||
@property
|
||||
def is_update_report(self): return global_config['is_update_report']
|
||||
|
||||
# 时间参数
|
||||
@property
|
||||
def start_year(self): return global_config['start_year']
|
||||
@property
|
||||
def end_time(self): return global_config['end_time']
|
||||
@property
|
||||
def freq(self): return global_config['freq']
|
||||
|
||||
# 接口配置
|
||||
@property
|
||||
def upload_url(self): return global_config['upload_url']
|
||||
|
||||
@property
|
||||
def login_pushreport_url(
|
||||
self): return global_config['login_pushreport_url']
|
||||
|
||||
@property
|
||||
def login_data(self): return global_config['login_data']
|
||||
@property
|
||||
def upload_headers(self): return global_config['upload_headers']
|
||||
@property
|
||||
def upload_warning_url(self): return global_config['upload_warning_url']
|
||||
@property
|
||||
def upload_warning_data(self): return global_config['upload_warning_data']
|
||||
|
||||
# 查询接口
|
||||
@property
|
||||
def query_data_list_item_nos_url(
|
||||
self): return global_config['query_data_list_item_nos_url']
|
||||
|
||||
@property
|
||||
def query_data_list_item_nos_data(
|
||||
self): return global_config['query_data_list_item_nos_data']
|
||||
|
||||
# 字段映射
|
||||
@property
|
||||
def offsite_col(self): return global_config['offsite_col']
|
||||
@property
|
||||
def avg_col(self): return global_config['avg_col']
|
||||
@property
|
||||
def offsite(self): return global_config['offsite']
|
||||
@property
|
||||
def edbcodenamedict(self): return global_config['edbcodenamedict']
|
||||
|
||||
# ETA配置
|
||||
@property
|
||||
def APPID(self): return global_config['APPID']
|
||||
@property
|
||||
def SECRET(self): return global_config['SECRET']
|
||||
|
||||
# 数据库配置
|
||||
@property
|
||||
def sqlitedb(self): return global_config['sqlitedb']
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
|
||||
class BinanceAPI:
|
||||
'''
|
||||
获取 Binance API 请求头签名
|
||||
@ -1397,7 +1585,8 @@ class EtaReader():
|
||||
|
||||
else:
|
||||
# 请求失败，打印错误信息
|
||||
logger.info(f'Error: {response.status_code}, {response.text}')
|
||||
config.logger.info(
|
||||
f'Error: {response.status_code}, {response.text}')
|
||||
# 主动抛出异常
|
||||
raise Exception(f'Error: {response.status_code}, {response.text}')
|
||||
|
||||
@ -1449,7 +1638,7 @@ class EtaReader():
|
||||
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'))
|
||||
# config.logger.info(data.get('Data'))
|
||||
# 定义你想要保留的固定值
|
||||
fixed_value = 1214
|
||||
|
||||
@ -1469,7 +1658,7 @@ class EtaReader():
|
||||
url = self.classifyidlisturl+str(ClassifyId)
|
||||
response = requests.get(url, headers=self.headers)
|
||||
if response.status_code == 200:
|
||||
# logger.info(response.text)
|
||||
# config.logger.info(response.text)
|
||||
data2 = response.json()
|
||||
Data = data2.get('Data')
|
||||
for i in Data:
|
||||
@ -1504,7 +1693,8 @@ class EtaReader():
|
||||
edbname_df = edbname_df.dropna()
|
||||
|
||||
if len(edbname_df) == 0:
|
||||
logger.info(f'指标名称：{EdbName} 没有数据')
|
||||
config.logger.info(
|
||||
f'指标名称：{EdbName} 没有数据')
|
||||
continue
|
||||
try:
|
||||
time_sequence = edbname_df['DataTime'].values.tolist(
|
||||
@ -1554,18 +1744,18 @@ class EtaReader():
|
||||
# df = pd.merge(df, df2, how='outer')
|
||||
df = pd.concat([df, df2])
|
||||
else:
|
||||
logger.info(f'跳过指标 {EdbName}')
|
||||
config.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)
|
||||
config.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)
|
||||
config.logger.info(item)
|
||||
# 将item 加入到 df['指标id']中
|
||||
try:
|
||||
itemname = edbcodenamedict[item]
|
||||
itemname = config.edbcodenamedict[item]
|
||||
except:
|
||||
itemname = item
|
||||
|
||||
@ -1579,8 +1769,8 @@ class EtaReader():
|
||||
# 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()}')
|
||||
config.logger.info(df1.head())
|
||||
# config.logger.info(f'{df1.head()}')
|
||||
|
||||
df_zhibiaoshuju = df1.copy()
|
||||
df_zhibiaoliebiao = df.copy()
|
||||
@ -1624,7 +1814,7 @@ class EtaReader():
|
||||
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'))
|
||||
# config.logger.info(data.get('Data'))
|
||||
# 定义你想要保留的固定值
|
||||
fixed_value = ClassifyId
|
||||
|
||||
@ -1644,7 +1834,7 @@ class EtaReader():
|
||||
url = self.classifyidlisturl+str(ClassifyId)
|
||||
response = requests.get(url, headers=self.headers)
|
||||
if response.status_code == 200:
|
||||
# logger.info(response.text)
|
||||
# config.logger.info(response.text)
|
||||
data2 = response.json()
|
||||
Data = data2.get('Data')
|
||||
for i in Data:
|
||||
@ -1669,18 +1859,18 @@ class EtaReader():
|
||||
df = pd.concat([df, df2])
|
||||
df1 = self.edbcodegetdata(df1, EdbCode, EdbName)
|
||||
else:
|
||||
logger.info(f'跳过指标 {EdbName}')
|
||||
config.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)
|
||||
config.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)
|
||||
config.logger.info(item)
|
||||
# 将item 加入到 df['指标id']中
|
||||
try:
|
||||
itemname = edbcodenamedict[item]
|
||||
itemname = config.edbcodenamedict[item]
|
||||
except:
|
||||
itemname = item
|
||||
|
||||
@ -1694,8 +1884,8 @@ class EtaReader():
|
||||
# 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()}')
|
||||
config.logger.info(df1.head())
|
||||
# config.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)
|
||||
@ -1719,7 +1909,7 @@ class EtaReader():
|
||||
}
|
||||
|
||||
# 发送post请求 上传数据
|
||||
logger.info(f'请求参数：{data}')
|
||||
config.logger.info(f'请求参数：{data}')
|
||||
response = requests.post(
|
||||
self.edbdatapushurl, headers=self.headers, data=json.dumps(data))
|
||||
|
||||
@ -1727,11 +1917,12 @@ class EtaReader():
|
||||
if response.status_code == 200:
|
||||
data = response.json() # 假设接口返回的是JSON数据
|
||||
|
||||
logger.info(f'上传成功，响应为：{data}')
|
||||
config.logger.info(f'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}')
|
||||
config.logger.info(
|
||||
f'Error: {response.status_code}, {response.text}')
|
||||
# 主动抛出异常
|
||||
raise Exception(f'Error: {response.status_code}, {response.text}')
|
||||
|
||||
@ -1758,11 +1949,12 @@ class EtaReader():
|
||||
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)
|
||||
config.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}')
|
||||
config.logger.info(
|
||||
f'Error: {response.status_code}, {response.text}')
|
||||
# 主动抛出异常
|
||||
raise Exception(f'Error: {response.status_code}, {response.text}')
|
||||
|
||||
@ -1797,11 +1989,12 @@ class EtaReader():
|
||||
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)
|
||||
config.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}')
|
||||
config.logger.info(
|
||||
f'Error: {response.status_code}, {response.text}')
|
||||
# 主动抛出异常
|
||||
raise Exception(f'Error: {response.status_code}, {response.text}')
|
||||
|
||||
@ -1813,15 +2006,15 @@ def get_market_data(end_time, df):
|
||||
# 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(
|
||||
config.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))
|
||||
config.logger.info('获取数据中...')
|
||||
items_res = requests.post(url=config.query_data_list_item_nos_url, headers=headers,
|
||||
json=config.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}")
|
||||
config.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()
|
||||
@ -1843,7 +2036,7 @@ def get_market_data(end_time, 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=[
|
||||
df1 = pd.read_excel(os.path.join(config.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')
|
||||
|
||||
@ -1,14 +1,67 @@
|
||||
# 读取配置
|
||||
|
||||
from lib.dataread import *
|
||||
# from config_jingbo_zhoudu import *
|
||||
from config_jingbo_zhoudu 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
|
||||
|
||||
import glob
|
||||
import datetime
|
||||
import torch
|
||||
torch.set_float32_matmul_precision("high")
|
||||
|
||||
global_config.update({
|
||||
# 核心参数
|
||||
'logger': logger,
|
||||
'dataset': dataset,
|
||||
'y': y,
|
||||
'is_debug': is_debug,
|
||||
'is_train': is_train,
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'settings': settings,
|
||||
|
||||
|
||||
# 模型参数
|
||||
'data_set': data_set,
|
||||
'input_size': input_size,
|
||||
'horizon': horizon,
|
||||
'train_steps': train_steps,
|
||||
'val_check_steps': val_check_steps,
|
||||
'val_size': val_size,
|
||||
'test_size': test_size,
|
||||
'modelsindex': modelsindex,
|
||||
'rote': rote,
|
||||
|
||||
# 特征工程开关
|
||||
'is_del_corr': is_del_corr,
|
||||
'is_del_tow_month': is_del_tow_month,
|
||||
'is_eta': is_eta,
|
||||
'is_update_eta': is_update_eta,
|
||||
'early_stop_patience_steps': early_stop_patience_steps,
|
||||
|
||||
# 时间参数
|
||||
'start_year': start_year,
|
||||
'end_time': end_time,
|
||||
'freq': freq, # 保持列表结构
|
||||
|
||||
# 接口配置
|
||||
'login_pushreport_url': login_pushreport_url,
|
||||
'login_data': login_data,
|
||||
'upload_url': upload_url,
|
||||
'upload_warning_url': upload_warning_url,
|
||||
'warning_data': warning_data,
|
||||
|
||||
# 查询接口
|
||||
'query_data_list_item_nos_url': query_data_list_item_nos_url,
|
||||
'query_data_list_item_nos_data': query_data_list_item_nos_data,
|
||||
|
||||
# eta 配置
|
||||
'APPID': APPID,
|
||||
'SECRET': SECRET,
|
||||
'etadata': data,
|
||||
|
||||
# 数据库配置
|
||||
'sqlitedb': sqlitedb,
|
||||
})
|
||||
|
||||
|
||||
def predict_main():
|
||||
"""
|
||||
@ -49,228 +102,237 @@ def predict_main():
|
||||
返回:
|
||||
None
|
||||
"""
|
||||
global end_time
|
||||
signature = BinanceAPI(APPID, SECRET)
|
||||
etadata = EtaReader(signature=signature,
|
||||
classifylisturl=classifylisturl,
|
||||
classifyidlisturl=classifyidlisturl,
|
||||
edbcodedataurl=edbcodedataurl,
|
||||
edbcodelist=edbcodelist,
|
||||
edbdatapushurl=edbdatapushurl,
|
||||
edbdeleteurl=edbdeleteurl,
|
||||
edbbusinessurl=edbbusinessurl
|
||||
)
|
||||
# 获取数据
|
||||
if is_eta:
|
||||
logger.info('从eta获取数据...')
|
||||
signature = BinanceAPI(APPID, SECRET)
|
||||
etadata = EtaReader(signature=signature,
|
||||
classifylisturl=classifylisturl,
|
||||
classifyidlisturl=classifyidlisturl,
|
||||
edbcodedataurl=edbcodedataurl,
|
||||
edbcodelist=edbcodelist,
|
||||
edbdatapushurl=edbdatapushurl,
|
||||
edbdeleteurl=edbdeleteurl,
|
||||
edbbusinessurl=edbbusinessurl,
|
||||
)
|
||||
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
|
||||
data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||
# global end_time
|
||||
# signature = BinanceAPI(APPID, SECRET)
|
||||
# etadata = EtaReader(signature=signature,
|
||||
# classifylisturl=classifylisturl,
|
||||
# classifyidlisturl=classifyidlisturl,
|
||||
# edbcodedataurl=edbcodedataurl,
|
||||
# edbcodelist=edbcodelist,
|
||||
# edbdatapushurl=edbdatapushurl,
|
||||
# edbdeleteurl=edbdeleteurl,
|
||||
# edbbusinessurl=edbbusinessurl
|
||||
# )
|
||||
# # 获取数据
|
||||
# if is_eta:
|
||||
# logger.info('从eta获取数据...')
|
||||
# signature = BinanceAPI(APPID, SECRET)
|
||||
# etadata = EtaReader(signature=signature,
|
||||
# classifylisturl=classifylisturl,
|
||||
# classifyidlisturl=classifyidlisturl,
|
||||
# edbcodedataurl=edbcodedataurl,
|
||||
# edbcodelist=edbcodelist,
|
||||
# edbdatapushurl=edbdatapushurl,
|
||||
# edbdeleteurl=edbdeleteurl,
|
||||
# edbbusinessurl=edbbusinessurl,
|
||||
# )
|
||||
# df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
|
||||
# data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||
|
||||
if is_market:
|
||||
logger.info('从市场信息平台获取数据...')
|
||||
try:
|
||||
# 如果是测试环境，最高价最低价取excel文档
|
||||
if server_host == '192.168.100.53':
|
||||
logger.info('从excel文档获取最高价最低价')
|
||||
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||
else:
|
||||
logger.info('从市场信息平台获取数据')
|
||||
df_zhibiaoshuju = get_market_data(
|
||||
end_time, df_zhibiaoshuju)
|
||||
|
||||
except:
|
||||
logger.info('最高最低价拼接失败')
|
||||
|
||||
# 保存到xlsx文件的sheet表
|
||||
with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
|
||||
df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
|
||||
df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
||||
|
||||
# 数据处理
|
||||
df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
||||
end_time=end_time)
|
||||
|
||||
else:
|
||||
# 读取数据
|
||||
logger.info('读取本地数据：' + os.path.join(dataset, data_set))
|
||||
df, df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
|
||||
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
||||
|
||||
# 更改预测列名称
|
||||
df.rename(columns={y: 'y'}, inplace=True)
|
||||
|
||||
if is_edbnamelist:
|
||||
df = df[edbnamelist]
|
||||
df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
|
||||
# 保存最新日期的y值到数据库
|
||||
# 取第一行数据存储到数据库中
|
||||
first_row = df[['ds', 'y']].tail(1)
|
||||
# 判断y的类型是否为float
|
||||
if not isinstance(first_row['y'].values[0], float):
|
||||
logger.info(f'{end_time}预测目标数据为空，跳过')
|
||||
return None
|
||||
|
||||
# 将最新真实值保存到数据库
|
||||
if not sqlitedb.check_table_exists('trueandpredict'):
|
||||
first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
|
||||
else:
|
||||
for row in first_row.itertuples(index=False):
|
||||
row_dict = row._asdict()
|
||||
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
||||
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=row_dict.keys())
|
||||
|
||||
# 更新accuracy表的y值
|
||||
if not sqlitedb.check_table_exists('accuracy'):
|
||||
pass
|
||||
else:
|
||||
update_y = sqlitedb.select_data(
|
||||
'accuracy', where_condition="y is null")
|
||||
if len(update_y) > 0:
|
||||
logger.info('更新accuracy表的y值')
|
||||
# 找到update_y 中ds且df中的y的行
|
||||
update_y = update_y[update_y['ds'] <= end_time]
|
||||
logger.info(f'要更新y的信息：{update_y}')
|
||||
# if is_market:
|
||||
# logger.info('从市场信息平台获取数据...')
|
||||
# try:
|
||||
for row in update_y.itertuples(index=False):
|
||||
try:
|
||||
row_dict = row._asdict()
|
||||
yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
|
||||
LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
|
||||
HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
|
||||
sqlitedb.update_data(
|
||||
'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||
except:
|
||||
logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
||||
# except Exception as e:
|
||||
# logger.info(f'更新accuracy表的y值失败：{e}')
|
||||
# # 如果是测试环境，最高价最低价取excel文档
|
||||
# if server_host == '192.168.100.53':
|
||||
# logger.info('从excel文档获取最高价最低价')
|
||||
# df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||
# else:
|
||||
# logger.info('从市场信息平台获取数据')
|
||||
# df_zhibiaoshuju = get_market_data(
|
||||
# end_time, df_zhibiaoshuju)
|
||||
|
||||
import datetime
|
||||
# 判断当前日期是不是周一
|
||||
is_weekday = datetime.datetime.now().weekday() == 0
|
||||
if is_weekday:
|
||||
logger.info('今天是周一，更新预测模型')
|
||||
# 计算最近60天预测残差最低的模型名称
|
||||
model_results = sqlitedb.select_data(
|
||||
'trueandpredict', order_by="ds DESC", limit="60")
|
||||
# 删除空值率为90%以上的列
|
||||
if len(model_results) > 10:
|
||||
model_results = model_results.dropna(
|
||||
thresh=len(model_results)*0.1, axis=1)
|
||||
# 删除空行
|
||||
model_results = model_results.dropna()
|
||||
modelnames = model_results.columns.to_list()[2:-1]
|
||||
for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||
model_results[col] = model_results[col].astype(np.float32)
|
||||
# 计算每个预测值与真实值之间的偏差率
|
||||
for model in modelnames:
|
||||
model_results[f'{model}_abs_error_rate'] = abs(
|
||||
model_results['y'] - model_results[model]) / model_results['y']
|
||||
# 获取每行对应的最小偏差率值
|
||||
min_abs_error_rate_values = model_results.apply(
|
||||
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].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 = model_results.apply(
|
||||
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].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])
|
||||
# 取出现次数最多的模型名称
|
||||
most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# 保存结果到数据库
|
||||
if not sqlitedb.check_table_exists('most_model'):
|
||||
sqlitedb.create_table(
|
||||
'most_model', columns="ds datetime, most_common_model TEXT")
|
||||
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||
'%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
# except:
|
||||
# logger.info('最高最低价拼接失败')
|
||||
|
||||
try:
|
||||
if is_weekday:
|
||||
# if True:
|
||||
logger.info('今天是周一，发送特征预警')
|
||||
# 上传预警信息到数据库
|
||||
warning_data_df = df_zhibiaoliebiao.copy()
|
||||
warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
|
||||
'指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
|
||||
# 重命名列名
|
||||
warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
|
||||
'更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
||||
from sqlalchemy import create_engine
|
||||
import urllib
|
||||
global password
|
||||
if '@' in password:
|
||||
password = urllib.parse.quote_plus(password)
|
||||
# # 保存到xlsx文件的sheet表
|
||||
# with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
|
||||
# df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
|
||||
# df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
||||
|
||||
engine = create_engine(
|
||||
f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
|
||||
"%Y-%m-%d %H:%M:%S")
|
||||
warning_data_df['TENANT_CODE'] = 'T0004'
|
||||
# 插入数据之前查询表数据然后新增id列
|
||||
existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
||||
if not existing_data.empty:
|
||||
max_id = existing_data['ID'].astype(int).max()
|
||||
warning_data_df['ID'] = range(
|
||||
max_id + 1, max_id + 1 + len(warning_data_df))
|
||||
else:
|
||||
warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
||||
warning_data_df.to_sql(
|
||||
table_name, con=engine, if_exists='append', index=False)
|
||||
if is_update_warning_data:
|
||||
upload_warning_info(len(warning_data_df))
|
||||
except:
|
||||
logger.info('上传预警信息到数据库失败')
|
||||
# # 数据处理
|
||||
# df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
||||
# end_time=end_time)
|
||||
|
||||
if is_corr:
|
||||
df = corr_feature(df=df)
|
||||
# else:
|
||||
# # 读取数据
|
||||
# logger.info('读取本地数据：' + os.path.join(dataset, data_set))
|
||||
# df, df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
|
||||
# is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
||||
|
||||
df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
|
||||
logger.info(f"开始训练模型...")
|
||||
row, col = df.shape
|
||||
# # 更改预测列名称
|
||||
# df.rename(columns={y: 'y'}, inplace=True)
|
||||
|
||||
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||
ex_Model(df,
|
||||
horizon=horizon,
|
||||
input_size=input_size,
|
||||
train_steps=train_steps,
|
||||
val_check_steps=val_check_steps,
|
||||
early_stop_patience_steps=early_stop_patience_steps,
|
||||
is_debug=is_debug,
|
||||
dataset=dataset,
|
||||
is_train=is_train,
|
||||
is_fivemodels=is_fivemodels,
|
||||
val_size=val_size,
|
||||
test_size=test_size,
|
||||
settings=settings,
|
||||
now=now,
|
||||
etadata=etadata,
|
||||
modelsindex=modelsindex,
|
||||
data=data,
|
||||
is_eta=is_eta,
|
||||
end_time=end_time,
|
||||
)
|
||||
# if is_edbnamelist:
|
||||
# df = df[edbnamelist]
|
||||
# df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
|
||||
# # 保存最新日期的y值到数据库
|
||||
# # 取第一行数据存储到数据库中
|
||||
# first_row = df[['ds', 'y']].tail(1)
|
||||
# # 判断y的类型是否为float
|
||||
# if not isinstance(first_row['y'].values[0], float):
|
||||
# logger.info(f'{end_time}预测目标数据为空，跳过')
|
||||
# return None
|
||||
|
||||
logger.info('模型训练完成')
|
||||
# # 将最新真实值保存到数据库
|
||||
# if not sqlitedb.check_table_exists('trueandpredict'):
|
||||
# first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
|
||||
# else:
|
||||
# for row in first_row.itertuples(index=False):
|
||||
# row_dict = row._asdict()
|
||||
# config.logger.info(f'要保存的真实值：{row_dict}')
|
||||
# # 判断ds是否为字符串类型,如果不是则转换为字符串类型
|
||||
# if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
|
||||
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||
# elif not isinstance(row_dict['ds'], str):
|
||||
# try:
|
||||
# row_dict['ds'] = pd.to_datetime(
|
||||
# row_dict['ds']).strftime('%Y-%m-%d')
|
||||
# except:
|
||||
# logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
|
||||
# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||
# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
||||
# 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=row_dict.keys())
|
||||
|
||||
# # 更新accuracy表的y值
|
||||
# if not sqlitedb.check_table_exists('accuracy'):
|
||||
# pass
|
||||
# else:
|
||||
# update_y = sqlitedb.select_data(
|
||||
# 'accuracy', where_condition="y is null")
|
||||
# if len(update_y) > 0:
|
||||
# logger.info('更新accuracy表的y值')
|
||||
# # 找到update_y 中ds且df中的y的行
|
||||
# update_y = update_y[update_y['ds'] <= end_time]
|
||||
# logger.info(f'要更新y的信息：{update_y}')
|
||||
# # try:
|
||||
# for row in update_y.itertuples(index=False):
|
||||
# try:
|
||||
# row_dict = row._asdict()
|
||||
# yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
|
||||
# LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
|
||||
# HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
|
||||
# sqlitedb.update_data(
|
||||
# 'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||
# except:
|
||||
# logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
||||
# # except Exception as e:
|
||||
# # logger.info(f'更新accuracy表的y值失败：{e}')
|
||||
|
||||
# # 判断当前日期是不是周一
|
||||
# is_weekday = datetime.datetime.now().weekday() == 0
|
||||
# if is_weekday:
|
||||
# logger.info('今天是周一，更新预测模型')
|
||||
# # 计算最近60天预测残差最低的模型名称
|
||||
# model_results = sqlitedb.select_data(
|
||||
# 'trueandpredict', order_by="ds DESC", limit="60")
|
||||
# # 删除空值率为90%以上的列
|
||||
# if len(model_results) > 10:
|
||||
# model_results = model_results.dropna(
|
||||
# thresh=len(model_results)*0.1, axis=1)
|
||||
# # 删除空行
|
||||
# model_results = model_results.dropna()
|
||||
# modelnames = model_results.columns.to_list()[2:-1]
|
||||
# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||
# model_results[col] = model_results[col].astype(np.float32)
|
||||
# # 计算每个预测值与真实值之间的偏差率
|
||||
# for model in modelnames:
|
||||
# model_results[f'{model}_abs_error_rate'] = abs(
|
||||
# model_results['y'] - model_results[model]) / model_results['y']
|
||||
# # 获取每行对应的最小偏差率值
|
||||
# min_abs_error_rate_values = model_results.apply(
|
||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].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 = model_results.apply(
|
||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].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])
|
||||
# # 取出现次数最多的模型名称
|
||||
# most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
# logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# # 保存结果到数据库
|
||||
# if not sqlitedb.check_table_exists('most_model'):
|
||||
# sqlitedb.create_table(
|
||||
# 'most_model', columns="ds datetime, most_common_model TEXT")
|
||||
# sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||
# '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
|
||||
# try:
|
||||
# if is_weekday:
|
||||
# # if True:
|
||||
# logger.info('今天是周一，发送特征预警')
|
||||
# # 上传预警信息到数据库
|
||||
# warning_data_df = df_zhibiaoliebiao.copy()
|
||||
# warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
|
||||
# '指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
|
||||
# # 重命名列名
|
||||
# warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
|
||||
# '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
||||
# from sqlalchemy import create_engine
|
||||
# import urllib
|
||||
# global password
|
||||
# if '@' in password:
|
||||
# password = urllib.parse.quote_plus(password)
|
||||
|
||||
# engine = create_engine(
|
||||
# f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||
# warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
|
||||
# "%Y-%m-%d %H:%M:%S")
|
||||
# warning_data_df['TENANT_CODE'] = 'T0004'
|
||||
# # 插入数据之前查询表数据然后新增id列
|
||||
# existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
||||
# if not existing_data.empty:
|
||||
# max_id = existing_data['ID'].astype(int).max()
|
||||
# warning_data_df['ID'] = range(
|
||||
# max_id + 1, max_id + 1 + len(warning_data_df))
|
||||
# else:
|
||||
# warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
||||
# warning_data_df.to_sql(
|
||||
# table_name, con=engine, if_exists='append', index=False)
|
||||
# if is_update_warning_data:
|
||||
# upload_warning_info(len(warning_data_df))
|
||||
# except:
|
||||
# logger.info('上传预警信息到数据库失败')
|
||||
|
||||
# if is_corr:
|
||||
# df = corr_feature(df=df)
|
||||
|
||||
# df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
|
||||
# logger.info(f"开始训练模型...")
|
||||
# row, col = df.shape
|
||||
|
||||
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||
# ex_Model(df,
|
||||
# horizon=global_config['horizon'],
|
||||
# input_size=global_config['input_size'],
|
||||
# train_steps=global_config['train_steps'],
|
||||
# val_check_steps=global_config['val_check_steps'],
|
||||
# early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
||||
# is_debug=global_config['is_debug'],
|
||||
# dataset=global_config['dataset'],
|
||||
# is_train=global_config['is_train'],
|
||||
# is_fivemodels=global_config['is_fivemodels'],
|
||||
# val_size=global_config['val_size'],
|
||||
# test_size=global_config['test_size'],
|
||||
# settings=global_config['settings'],
|
||||
# now=now,
|
||||
# etadata=global_config['etadata'],
|
||||
# modelsindex=global_config['modelsindex'],
|
||||
# data=data,
|
||||
# is_eta=global_config['is_eta'],
|
||||
# end_time=global_config['end_time'],
|
||||
# )
|
||||
|
||||
# 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)
|
||||
|
||||
@ -112,11 +112,11 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
|
||||
df_test['unique_id'] = 1
|
||||
|
||||
# 显示划分后的数据集的前几行
|
||||
logger.info("Training set head:")
|
||||
logger.info(df_train.head())
|
||||
config.logger.info("Training set head:")
|
||||
config.logger.info(df_train.head())
|
||||
|
||||
logger.info("\nTesting set head:")
|
||||
logger.info(df_test.head())
|
||||
config.logger.info("\nTesting set head:")
|
||||
config.logger.info(df_test.head())
|
||||
|
||||
models = [
|
||||
NHITS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps,
|
||||
@ -185,7 +185,8 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
|
||||
models.append(model)
|
||||
|
||||
# 创建NeuralForecast实例并训练模型
|
||||
nf = NeuralForecast(models=models, freq=freq[0])
|
||||
# freq = 'B'
|
||||
nf = NeuralForecast(models=models, freq=config.freq[:1])
|
||||
|
||||
from joblib import dump, load
|
||||
if is_train:
|
||||
@ -207,7 +208,7 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
|
||||
import glob
|
||||
filename = max(glob.glob(os.path.join(
|
||||
dataset, '*.joblib')), key=os.path.getctime)
|
||||
logger.info('读取模型：' + filename)
|
||||
config.logger.info('读取模型：' + filename)
|
||||
nf = load(filename)
|
||||
# 测试集预测
|
||||
nf_test_preds = nf.cross_validation(
|
||||
@ -233,10 +234,10 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
|
||||
df_predict.to_csv(os.path.join(dataset, "predict.csv"), index=False)
|
||||
|
||||
# 将预测结果保存到数据库
|
||||
save_to_database(sqlitedb, df_predict, 'predict', end_time)
|
||||
save_to_database(config.sqlitedb, df_predict, 'predict', end_time)
|
||||
|
||||
# 把预测值上传到eta
|
||||
if is_update_eta:
|
||||
if config.is_update_eta:
|
||||
df_predict['ds'] = pd.to_datetime(df_predict['ds'])
|
||||
dates = df_predict['ds'].dt.strftime('%Y-%m-%d')
|
||||
|
||||
@ -942,12 +943,12 @@ def model_losss(sqlitedb, end_time):
|
||||
df_combined['CREAT_DATE'] = df_combined['cutoff']
|
||||
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)
|
||||
config.logger.info(df_combined.shape)
|
||||
df_combined = df_combined.loc[:, df_combined.isnull().mean() < 0.8]
|
||||
logger.info(df_combined.shape)
|
||||
config.logger.info(df_combined.shape)
|
||||
# 删除缺失值
|
||||
df_combined.dropna(inplace=True)
|
||||
logger.info(df_combined.shape)
|
||||
config.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', 'cutoff']})
|
||||
@ -989,17 +990,17 @@ def model_losss(sqlitedb, end_time):
|
||||
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)
|
||||
config.dataset, "model_evaluation.csv"), index=False)
|
||||
modelnames = model_results3['模型(Model)'].tolist()
|
||||
most_model_name = modelnames[0]
|
||||
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:
|
||||
if config.is_fivemodels:
|
||||
pass
|
||||
else:
|
||||
with open(os.path.join(dataset, "best_modelnames.txt"), 'w') as f:
|
||||
with open(os.path.join(config.dataset, "best_modelnames.txt"), 'w') as f:
|
||||
f.write(','.join(modelnames) + '\n')
|
||||
|
||||
# 预测值与真实值对比图
|
||||
@ -1014,12 +1015,13 @@ def model_losss(sqlitedb, end_time):
|
||||
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.savefig(os.path.join(config.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 = pd.read_csv(os.path.join(config.dataset, 'predict.csv'))
|
||||
df_predict.drop('unique_id', inplace=True, axis=1)
|
||||
df_predict.dropna(axis=1, inplace=True)
|
||||
|
||||
@ -1071,7 +1073,7 @@ def model_losss(sqlitedb, end_time):
|
||||
def add_rote_column(row):
|
||||
columns = []
|
||||
for r in names_df.columns:
|
||||
if row[r] <= rote:
|
||||
if row[r] <= config.rote:
|
||||
columns.append(r.split('-')[0])
|
||||
return pd.Series([columns], index=['columns'])
|
||||
names_df['columns'] = names_df.apply(add_rote_column, axis=1)
|
||||
@ -1124,7 +1126,7 @@ def model_losss(sqlitedb, end_time):
|
||||
|
||||
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)
|
||||
df_predict2 = df_combined3.tail(config.horizon)
|
||||
|
||||
# 保存到数据库
|
||||
if not sqlitedb.check_table_exists('accuracy'):
|
||||
@ -1167,7 +1169,8 @@ def model_losss(sqlitedb, end_time):
|
||||
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}')
|
||||
config.loggererror(
|
||||
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'])
|
||||
@ -1256,7 +1259,7 @@ def model_losss(sqlitedb, end_time):
|
||||
except ValueError:
|
||||
pass
|
||||
df_combined3.to_csv(os.path.join(
|
||||
dataset, "testandpredict_groupby.csv"), index=False)
|
||||
config.dataset, "testandpredict_groupby.csv"), index=False)
|
||||
|
||||
# 历史价格+预测价格
|
||||
sqlitedb.drop_table('testandpredict_groupby')
|
||||
@ -1301,7 +1304,8 @@ def model_losss(sqlitedb, end_time):
|
||||
plt.xticks(rotation=45) # 日期标签旋转45度，防止重叠
|
||||
plt.ylabel('价格')
|
||||
|
||||
plt.savefig(os.path.join(dataset, '历史价格-预测值.png'), bbox_inches='tight')
|
||||
plt.savefig(os.path.join(config.dataset, '历史价格-预测值.png'),
|
||||
bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
def _plt_modeltopten_predict_ture(df):
|
||||
@ -1340,7 +1344,7 @@ def model_losss(sqlitedb, end_time):
|
||||
|
||||
plt.ylabel('价格')
|
||||
|
||||
plt.savefig(os.path.join(dataset, '历史价格-预测值1.png'),
|
||||
plt.savefig(os.path.join(config.dataset, '历史价格-预测值1.png'),
|
||||
bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
@ -1361,7 +1365,8 @@ def model_losss(sqlitedb, end_time):
|
||||
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.savefig(os.path.join(config.dataset, '预测值表格.png'),
|
||||
bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
def _plt_model_results3():
|
||||
@ -1376,7 +1381,8 @@ def model_losss(sqlitedb, end_time):
|
||||
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.savefig(os.path.join(config.dataset, '模型评估.png'),
|
||||
bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# _plt_predict_ture(df_combined3)
|
||||
@ -2198,7 +2204,7 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
|
||||
# print(f'绘制第{i+1}个特征{col}与价格散点图时出错：{e}')
|
||||
|
||||
# 添加标题
|
||||
content.append(Graphs.draw_title(f'{y}{time}预测报告'))
|
||||
content.append(Graphs.draw_title(f'{config.y}{time}预测报告'))
|
||||
|
||||
# 预测结果
|
||||
content.append(Graphs.draw_little_title('一、预测结果：'))
|
||||
@ -2321,7 +2327,7 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
|
||||
'''相关系数接近0：表示两个变量之间不存在线性关系，即它们的变化不会随着对方的变化而变化。'''))
|
||||
for name, group in grouped:
|
||||
cols = group['指标名称'].tolist()
|
||||
logger.info(f'开始绘制{name}类指标的相关性直方图')
|
||||
config.logger.info(f'开始绘制{name}类指标的相关性直方图')
|
||||
cols_subset = cols
|
||||
feature_names = ['y'] + cols_subset
|
||||
correlation_matrix = df_zhibiaoshuju[feature_names].corr()['y']
|
||||
@ -2376,7 +2382,7 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
|
||||
draw_feature_trend(feature_data_df, negative_corr_features)
|
||||
# 计算correlation_sum 第一行的相关性的绝对值的总和
|
||||
correlation_sum = correlation_matrix.abs().sum()
|
||||
logger.info(f'{name}类指标的相关性总和为：{correlation_sum}')
|
||||
config.logger.info(f'{name}类指标的相关性总和为：{correlation_sum}')
|
||||
# 分组的相关性总和拼接到grouped_corr
|
||||
goup_corr = pd.DataFrame(
|
||||
{'指标分类': [name], '指标数量': [len(cols_subset)], '相关性总和': [correlation_sum]})
|
||||
@ -2384,7 +2390,7 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
|
||||
[grouped_corr, goup_corr], axis=0, ignore_index=True)
|
||||
|
||||
# 绘制相关性总和的气泡图
|
||||
logger.info(f'开始绘制相关性总和的气泡图')
|
||||
config.logger.info(f'开始绘制相关性总和的气泡图')
|
||||
plt.figure(figsize=(10, 10))
|
||||
sns.scatterplot(data=grouped_corr, x='相关性总和', y='指标数量', size='相关性总和', sizes=(
|
||||
grouped_corr['相关性总和'].min()*5, grouped_corr['相关性总和'].max()*5), hue='指标分类', palette='viridis')
|
||||
@ -2396,7 +2402,7 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
|
||||
content.append(Graphs.draw_img(os.path.join(dataset, '指标分类相关性总和的气泡图.png')))
|
||||
content.append(Graphs.draw_text(
|
||||
'气泡图中，横轴为指标分类，纵轴为指标分类下的特征数量，气泡的面积越大表示该分类中特征的相关系数和越大。'))
|
||||
logger.info(f'绘制相关性总和的气泡图结束')
|
||||
config.logger.info(f'绘制相关性总和的气泡图结束')
|
||||
content.append(Graphs.draw_little_title('模型选择：'))
|
||||
content.append(Graphs.draw_text(
|
||||
f'预测使用了{num_models}个模型进行训练拟合，通过评估指标MAE从小到大排列，前5个模型的简介如下：'))
|
||||
@ -2441,7 +2447,7 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
|
||||
doc.build(content)
|
||||
# pdf 上传到数字化信息平台
|
||||
try:
|
||||
if is_update_report:
|
||||
if config.is_update_report:
|
||||
with open(os.path.join(dataset, reportname), 'rb') as f:
|
||||
base64_data = base64.b64encode(f.read()).decode('utf-8')
|
||||
upload_data["data"]["fileBase64"] = base64_data
|
||||
|
||||
Loading…
Reference in New Issue
Block a user