原油预测，推送次日，本周数据到北京测试环境
This commit is contained in:
jingboyitiji
parent
7e71c8e1c8
commit
c46709cef9
@ -98,6 +98,7 @@ login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
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upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave" # 上传报告
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upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save" # 停更预警
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query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos" # 查询数据项编码
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push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList" # 上传数据项值
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login_data = {
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"data": {
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@ -150,6 +151,33 @@ query_data_list_item_nos_data = {
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}
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}
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push_data_value_list_data = {
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"funcModule": "数据表信息列表",
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"funcOperation": "新增",
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"data": [
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{"dataItemNo":"91230600716676129",
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"dataDate":"20230113",
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"dataStatus":"add",
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"dataValue":100.11
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},
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{"dataItemNo":"91230600716676129P|ETHYL_BEN|CAPACITY",
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"dataDate":"20230113",
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"dataStatus":"add",
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"dataValue":100.55
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},
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{"dataItemNo":"91230600716676129P|ETHYL_BEN|CAPACITY",
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"dataDate":"20230113",
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"dataStatus":"add",
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"dataValue":100.55
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}
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]
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}
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# 八大维度数据项编码
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bdwd_items = {
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'ciri' : 'yyycbdwdcr',
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'benzhou': 'yyycbdwdbz',
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}
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# 北京环境数据库
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host = '192.168.101.27'
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@ -172,6 +200,7 @@ is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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is_update_eta = False # 预测结果上传到eta
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is_update_report = False # 是否上传报告
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is_update_warning_data = False # 是否上传预警数据
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is_update_predict_value = True # 是否上传预测值到市场信息平台
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is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
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is_del_tow_month = True # 是否删除两个月不更新的特征
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@ -94,6 +94,7 @@ global_config = {
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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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'bdwd_items':None,
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# 接口配置（原有配置）
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'login_pushreport_url': None,
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@ -1163,6 +1164,11 @@ class Config:
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def query_data_list_item_nos_data(
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self): return global_config['query_data_list_item_nos_data']
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@property
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def push_data_value_list_url(self): return global_config['push_data_value_list_url']
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@property
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def push_data_value_list_data(self): return global_config['push_data_value_list_data']
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# 字段映射
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@property
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def offsite_col(self): return global_config['offsite_col']
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@ -2030,6 +2036,40 @@ def get_market_data(end_time, df):
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df = pd.merge(df, df2, how='left', on='date')
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return df
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def push_market_data(data):
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'''
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上传预测价格到市场信息平台
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data: 预测价格数据,示例：
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[
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{"dataItemNo":"91230600716676129",
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"dataDate":"20230113",
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"dataStatus":"add",
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"dataValue":100.11
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},
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{"dataItemNo":"91230600716676129P|ETHYL_BEN|CAPACITY",
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"dataDate":"20230113",
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"dataStatus":"add",
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"dataValue":100.55
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},
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{"dataItemNo":"91230600716676129P|ETHYL_BEN|CAPACITY",
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"dataDate":"20230113",
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"dataStatus":"add",
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"dataValue":100.55
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}
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]
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'''
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# 获取token
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token = get_head_auth_report()
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# 定义请求参数
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config.push_data_value_list_data['data'] = data
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# 发送请求
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headers = {"Authorization": token}
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config.logger.info('上传数据中...')
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items_res = requests.post(url=config.push_data_value_list_url, headers=headers,
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json=config.push_data_value_list_data, timeout=(3, 35))
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json_data = json.loads(items_res.text)
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config.logger.info(f"上传结果:{json_data}")
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return json_data
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def get_high_low_data(df):
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# 读取excel 从第五行开始
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487
main_yuanyou.py
487
main_yuanyou.py
@ -29,6 +29,7 @@ global_config.update({
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'test_size': test_size,
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'modelsindex': modelsindex,
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'rote': rote,
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'bdwd_items':bdwd_items,
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# 特征工程开关
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'is_del_corr': is_del_corr,
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@ -36,6 +37,7 @@ global_config.update({
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'is_eta': is_eta,
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'is_update_eta': is_update_eta,
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'is_fivemodels': is_fivemodels,
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'is_update_predict_value': is_update_predict_value,
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'early_stop_patience_steps': early_stop_patience_steps,
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# 时间参数
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@ -112,243 +114,296 @@ def predict_main():
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返回:
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None
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"""
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end_time = global_config['end_time']
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# 获取数据
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if is_eta:
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logger.info('从eta获取数据...')
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signature = BinanceAPI(APPID, SECRET)
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etadata = EtaReader(signature=signature,
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classifylisturl=global_config['classifylisturl'],
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classifyidlisturl=global_config['classifyidlisturl'],
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edbcodedataurl=global_config['edbcodedataurl'],
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edbcodelist=global_config['edbcodelist'],
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edbdatapushurl=global_config['edbdatapushurl'],
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edbdeleteurl=global_config['edbdeleteurl'],
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edbbusinessurl=global_config['edbbusinessurl'],
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classifyId=global_config['ClassifyId'],
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)
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df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
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data_set=data_set, dataset=dataset) # 原始数据，未处理
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# end_time = global_config['end_time']
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# # 获取数据
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# if is_eta:
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# logger.info('从eta获取数据...')
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# signature = BinanceAPI(APPID, SECRET)
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# etadata = EtaReader(signature=signature,
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# classifylisturl=global_config['classifylisturl'],
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# classifyidlisturl=global_config['classifyidlisturl'],
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# edbcodedataurl=global_config['edbcodedataurl'],
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# edbcodelist=global_config['edbcodelist'],
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# edbdatapushurl=global_config['edbdatapushurl'],
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# edbdeleteurl=global_config['edbdeleteurl'],
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# edbbusinessurl=global_config['edbbusinessurl'],
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# classifyId=global_config['ClassifyId'],
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# )
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# df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
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# data_set=data_set, dataset=dataset) # 原始数据，未处理
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if is_market:
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logger.info('从市场信息平台获取数据...')
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try:
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# 如果是测试环境，最高价最低价取excel文档
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if server_host == '192.168.100.53':
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logger.info('从excel文档获取最高价最低价')
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df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
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else:
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logger.info('从市场信息平台获取数据')
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df_zhibiaoshuju = get_market_data(
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end_time, df_zhibiaoshuju)
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# if is_market:
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# logger.info('从市场信息平台获取数据...')
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# try:
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# # 如果是测试环境，最高价最低价取excel文档
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# if server_host == '192.168.100.53':
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# logger.info('从excel文档获取最高价最低价')
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# df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
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# else:
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# logger.info('从市场信息平台获取数据')
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# df_zhibiaoshuju = get_market_data(
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# end_time, df_zhibiaoshuju)
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except:
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logger.info('最高最低价拼接失败')
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# except:
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# logger.info('最高最低价拼接失败')
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# 保存到xlsx文件的sheet表
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with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
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df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
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df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
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# # 保存到xlsx文件的sheet表
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# with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
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# df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
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# df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
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# 数据处理
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df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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end_time=end_time)
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# # 数据处理
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# df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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# end_time=end_time)
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else:
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# 读取数据
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logger.info('读取本地数据：' + os.path.join(dataset, data_set))
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df, df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
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is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
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# else:
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# # 读取数据
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# logger.info('读取本地数据：' + os.path.join(dataset, data_set))
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# df, df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
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# is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
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# 更改预测列名称
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df.rename(columns={y: 'y'}, inplace=True)
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# # 更改预测列名称
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# df.rename(columns={y: 'y'}, inplace=True)
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if is_edbnamelist:
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df = df[edbnamelist]
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df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
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# 保存最新日期的y值到数据库
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# 取第一行数据存储到数据库中
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first_row = df[['ds', 'y']].tail(1)
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# 判断y的类型是否为float
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if not isinstance(first_row['y'].values[0], float):
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logger.info(f'{end_time}预测目标数据为空，跳过')
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return None
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# if is_edbnamelist:
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# df = df[edbnamelist]
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# df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
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# # 保存最新日期的y值到数据库
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# # 取第一行数据存储到数据库中
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# first_row = df[['ds', 'y']].tail(1)
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# # 判断y的类型是否为float
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# if not isinstance(first_row['y'].values[0], float):
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# logger.info(f'{end_time}预测目标数据为空，跳过')
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# return None
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# 将最新真实值保存到数据库
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if not sqlitedb.check_table_exists('trueandpredict'):
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first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
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else:
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for row in first_row.itertuples(index=False):
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row_dict = row._asdict()
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config.logger.info(f'要保存的真实值：{row_dict}')
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# 判断ds是否为字符串类型,如果不是则转换为字符串类型
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if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
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row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
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elif not isinstance(row_dict['ds'], str):
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try:
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row_dict['ds'] = pd.to_datetime(
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row_dict['ds']).strftime('%Y-%m-%d')
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except:
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logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
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# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
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# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
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check_query = sqlitedb.select_data(
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'trueandpredict', where_condition=f"ds = '{row.ds}'")
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if len(check_query) > 0:
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set_clause = ", ".join(
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[f"{key} = '{value}'" for key, value in row_dict.items()])
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sqlitedb.update_data(
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'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
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continue
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sqlitedb.insert_data('trueandpredict', tuple(
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row_dict.values()), columns=row_dict.keys())
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# # 将最新真实值保存到数据库
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# if not sqlitedb.check_table_exists('trueandpredict'):
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# first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
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# else:
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# for row in first_row.itertuples(index=False):
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# row_dict = row._asdict()
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# config.logger.info(f'要保存的真实值：{row_dict}')
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# # 判断ds是否为字符串类型,如果不是则转换为字符串类型
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# if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
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# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
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# elif not isinstance(row_dict['ds'], str):
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# try:
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# row_dict['ds'] = pd.to_datetime(
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# row_dict['ds']).strftime('%Y-%m-%d')
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# except:
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# logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
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# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
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# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
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# check_query = sqlitedb.select_data(
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# 'trueandpredict', where_condition=f"ds = '{row.ds}'")
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# if len(check_query) > 0:
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# set_clause = ", ".join(
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# [f"{key} = '{value}'" for key, value in row_dict.items()])
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# sqlitedb.update_data(
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# 'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
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# continue
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# sqlitedb.insert_data('trueandpredict', tuple(
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# row_dict.values()), columns=row_dict.keys())
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# 更新accuracy表的y值
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if not sqlitedb.check_table_exists('accuracy'):
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pass
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else:
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update_y = sqlitedb.select_data(
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'accuracy', where_condition="y is null")
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if len(update_y) > 0:
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logger.info('更新accuracy表的y值')
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# 找到update_y 中ds且df中的y的行
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update_y = update_y[update_y['ds'] <= end_time]
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logger.info(f'要更新y的信息：{update_y}')
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# try:
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for row in update_y.itertuples(index=False):
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try:
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row_dict = row._asdict()
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yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
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LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
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HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
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sqlitedb.update_data(
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'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
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except:
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logger.info(f'更新accuracy表的y值失败：{row_dict}')
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# except Exception as e:
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# logger.info(f'更新accuracy表的y值失败：{e}')
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# # 更新accuracy表的y值
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# if not sqlitedb.check_table_exists('accuracy'):
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# pass
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# else:
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# update_y = sqlitedb.select_data(
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# 'accuracy', where_condition="y is null")
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# if len(update_y) > 0:
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# logger.info('更新accuracy表的y值')
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# # 找到update_y 中ds且df中的y的行
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# update_y = update_y[update_y['ds'] <= end_time]
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# logger.info(f'要更新y的信息：{update_y}')
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# # try:
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# for row in update_y.itertuples(index=False):
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# try:
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# row_dict = row._asdict()
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# yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
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# LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
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# HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
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# sqlitedb.update_data(
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# 'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
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# except:
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# logger.info(f'更新accuracy表的y值失败：{row_dict}')
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# # except Exception as e:
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||||
# # 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',))
|
||||
# # 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)
|
||||
# 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')
|
||||
# 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)
|
||||
# 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
|
||||
# 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'],
|
||||
)
|
||||
# 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)
|
||||
logger.info('训练数据绘图end')
|
||||
|
||||
|
||||
# logger.info('训练数据绘图ing')
|
||||
# model_results3 = model_losss(sqlitedb, end_time=end_time)
|
||||
# logger.info('训练数据绘图end')
|
||||
|
||||
# # 模型报告
|
||||
logger.info('制作报告ing')
|
||||
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
reportname = f'Brent原油大模型日度预测--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||
reportname=reportname, sqlitedb=sqlitedb),
|
||||
# logger.info('制作报告ing')
|
||||
# title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
# reportname = f'Brent原油大模型日度预测--{end_time}.pdf' # 报告文件名
|
||||
# reportname = reportname.replace(':', '-') # 替换冒号
|
||||
# brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||
# reportname=reportname, sqlitedb=sqlitedb),
|
||||
|
||||
logger.info('制作报告end')
|
||||
logger.info('模型训练完成')
|
||||
# logger.info('制作报告end')
|
||||
# logger.info('模型训练完成')
|
||||
|
||||
logger.info('发送预测结果到市场信息平台')
|
||||
# 读取预测数据和模型评估数据
|
||||
predict_file_path = os.path.join(config.dataset, 'predict.csv')
|
||||
model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
|
||||
try:
|
||||
predictdata_df = pd.read_csv(predict_file_path)
|
||||
top_models_df = pd.read_csv(model_eval_file_path)
|
||||
except FileNotFoundError as e:
|
||||
logger.error(f"文件未找到: {e}")
|
||||
return
|
||||
|
||||
predictdata = predictdata_df.copy()
|
||||
|
||||
# 取模型前十
|
||||
top_models = top_models_df['模型(Model)'].head(10).tolist()
|
||||
|
||||
# 计算前十模型的均值
|
||||
predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
|
||||
|
||||
# 打印日期和前十模型均值
|
||||
print(predictdata_df[['ds', 'top_models_mean']])
|
||||
|
||||
# 准备要推送的数据
|
||||
first_date = predictdata_df['ds'].iloc[0].replace('-', '')
|
||||
last_date = predictdata_df['ds'].iloc[-1].replace('-', '')
|
||||
first_mean = predictdata_df['top_models_mean'].iloc[0]
|
||||
last_mean = predictdata_df['top_models_mean'].iloc[-1]
|
||||
|
||||
predictdata = [
|
||||
{
|
||||
"dataItemNo": global_config['bdwd_items']['ciri'],
|
||||
"dataDate": first_date,
|
||||
"dataStatus": "add",
|
||||
"dataValue": first_mean
|
||||
},
|
||||
{
|
||||
"dataItemNo": global_config['bdwd_items']['benzhou'],
|
||||
"dataDate": last_date,
|
||||
"dataStatus": "add",
|
||||
"dataValue": last_mean
|
||||
}
|
||||
]
|
||||
|
||||
print(predictdata)
|
||||
|
||||
# 推送数据到市场信息平台
|
||||
try:
|
||||
push_market_data(predictdata)
|
||||
except Exception as e:
|
||||
logger.error(f"推送数据失败: {e}")
|
||||
|
||||
# # LSTM 单变量模型
|
||||
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
||||
|
||||
@ -280,7 +280,7 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "base",
|
||||
"display_name": "predict",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -294,7 +294,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.7"
|
||||
"version": "3.11.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user