月度配置更新预测结果上传到市场信息平台
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@ -90,8 +90,8 @@ ClassifyId = 1214
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# 变量定义--测试环境
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server_host = '192.168.100.53'
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# server_host = '192.168.100.53' # 内网
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server_host = '183.242.74.28 ' # 外网
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login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
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# 上传报告
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upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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@ -90,15 +90,14 @@ ClassifyId = 1214
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# 变量定义--测试环境
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server_host = '192.168.100.53'
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# server_host = '192.168.100.53' # 内网
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server_host = '183.242.74.28 ' # 外网
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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"
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upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
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query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
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# 数据项推送接口
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push_data_balue_list = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList"
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# 上传数据项值
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push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList"
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login_data = {
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"data": {
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@ -113,6 +112,7 @@ login_data = {
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}
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upload_data = {
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"groupNo": '', # 用户组id
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"funcModule": '研究报告信息',
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"funcOperation": '上传原油价格预测报告',
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"data": {
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@ -130,6 +130,7 @@ upload_data = {
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warning_data = {
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"groupNo": '', # 用户组id
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"funcModule": '原油特征停更预警',
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"funcOperation": '原油特征停更预警',
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"data": {
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@ -149,6 +150,39 @@ 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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'cizhou': 'yyycbdwdcz',
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'gezhou': 'yyycbdwdgz',
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'ciyue': 'yyycbdwdcy',
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'cieryue': 'yyycbdwdcey',
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'cisanyue': 'yyycbdwdcsy',
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'cisiyue': 'yyycbdwdcsiy',
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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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@ -171,6 +205,7 @@ 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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@ -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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@ -54,6 +56,10 @@ global_config.update({
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'query_data_list_item_nos_url': query_data_list_item_nos_url,
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'query_data_list_item_nos_data': query_data_list_item_nos_data,
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# 上传数据项
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'push_data_value_list_url': push_data_value_list_url,
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'push_data_value_list_data': push_data_value_list_data,
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# eta 配置
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'APPID': APPID,
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'SECRET': SECRET,
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@ -73,6 +79,75 @@ global_config.update({
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})
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def push_market_value():
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logger.info('发送预测结果到市场信息平台')
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# 读取预测数据和模型评估数据
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predict_file_path = os.path.join(config.dataset, 'predict.csv')
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model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
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try:
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predictdata_df = pd.read_csv(predict_file_path)
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top_models_df = pd.read_csv(model_eval_file_path)
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except FileNotFoundError as e:
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logger.error(f"文件未找到: {e}")
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return
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predictdata = predictdata_df.copy()
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# 取模型前十
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top_models = top_models_df['模型(Model)'].head(10).tolist()
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# 计算前十模型的均值
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predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
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# 打印日期和前十模型均值
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print(predictdata_df[['ds', 'top_models_mean']])
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# 准备要推送的数据
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ciyue_date = predictdata_df['ds'].iloc[0].replace('-', '')
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cieryue_date = predictdata_df['ds'].iloc[1].replace('-', '')
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cisanyue_date = predictdata_df['ds'].iloc[2].replace('-', '')
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cisiyue_date = predictdata_df['ds'].iloc[3].replace('-', '')
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ciyue_mean = predictdata_df['top_models_mean'].iloc[0]
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cieryue_mean = predictdata_df['top_models_mean'].iloc[1]
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cisanyue_mean = predictdata_df['top_models_mean'].iloc[2]
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cisieryue_mean = predictdata_df['top_models_mean'].iloc[3]
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predictdata = [
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{
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"dataItemNo": global_config['bdwd_items']['ciyue'],
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"dataDate": ciyue_date,
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"dataStatus": "add",
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"dataValue": ciyue_mean
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},
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{
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"dataItemNo": global_config['bdwd_items']['cieryue'],
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"dataDate": cieryue_date,
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"dataStatus": "add",
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"dataValue": cieryue_mean
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},
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{
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"dataItemNo": global_config['bdwd_items']['cisanyue'],
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"dataDate": cisanyue_date,
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"dataStatus": "add",
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"dataValue": cisanyue_mean
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},
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{
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"dataItemNo": global_config['bdwd_items']['cisiyue'],
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"dataDate": cisiyue_date,
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"dataStatus": "add",
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"dataValue": cisieryue_mean
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}
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]
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print(predictdata)
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# 推送数据到市场信息平台
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try:
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push_market_data(predictdata)
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except Exception as e:
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logger.error(f"推送数据失败: {e}")
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def predict_main():
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"""
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主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。
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@ -113,28 +188,19 @@ def predict_main():
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None
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"""
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end_time = global_config['end_time']
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signature = BinanceAPI(APPID, SECRET)
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etadata = EtaReader(signature=signature,
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classifylisturl=classifylisturl,
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classifyidlisturl=classifyidlisturl,
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edbcodedataurl=edbcodedataurl,
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edbcodelist=edbcodelist,
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edbdatapushurl=edbdatapushurl,
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edbdeleteurl=edbdeleteurl,
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edbbusinessurl=edbbusinessurl
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)
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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=classifylisturl,
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classifyidlisturl=classifyidlisturl,
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edbcodedataurl=edbcodedataurl,
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edbcodelist=edbcodelist,
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edbdatapushurl=edbdatapushurl,
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edbdeleteurl=edbdeleteurl,
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edbbusinessurl=edbbusinessurl,
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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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@ -348,16 +414,18 @@ def predict_main():
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model_results3 = model_losss(sqlitedb, end_time=end_time)
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logger.info('训练数据绘图end')
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# # 模型报告
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# logger.info('制作报告ing')
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# title = f'{settings}--{end_time}-预测报告' # 报告标题
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# reportname = f'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名
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# reportname = reportname.replace(':', '-') # 替换冒号
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# brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
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# reportname=reportname, sqlitedb=sqlitedb),
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# 模型报告
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logger.info('制作报告ing')
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title = f'{settings}--{end_time}-预测报告' # 报告标题
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reportname = f'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名
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reportname = reportname.replace(':', '-') # 替换冒号
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brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
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reportname=reportname, sqlitedb=sqlitedb),
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# logger.info('制作报告end')
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# logger.info('模型训练完成')
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logger.info('制作报告end')
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logger.info('模型训练完成')
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push_market_value()
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# # LSTM 单变量模型
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# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
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