更改上传数据日期格式
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@ -198,7 +198,7 @@ table_name = 'v_tbl_crude_oil_warning'
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# 开关
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is_train = True # 是否训练
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is_debug = True # 是否调试
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is_debug = False # 是否调试
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is_eta = True # 是否使用eta接口
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is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
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is_timefurture = True # 是否使用时间特征
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@ -206,7 +206,7 @@ is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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is_edbcode = False # 特征使用edbcoding列表中的
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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_report = True # 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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@ -194,7 +194,7 @@ table_name = 'v_tbl_crude_oil_warning'
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# 开关
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is_train = False # 是否训练
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is_train = True # 是否训练
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is_debug = False # 是否调试
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is_eta = True # 是否使用eta接口
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is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
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@ -203,7 +203,7 @@ is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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is_edbcode = False # 特征使用edbcoding列表中的
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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_report = True # 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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@ -195,14 +195,14 @@ table_name = 'v_tbl_crude_oil_warning'
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# 开关
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is_train = True # 是否训练
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is_debug = False # 是否调试
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is_eta = False # 是否使用eta接口
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is_eta = True # 是否使用eta接口
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is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
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is_timefurture = True # 是否使用时间特征
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is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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is_edbcode = False # 特征使用edbcoding列表中的
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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_report = True # 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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@ -105,17 +105,20 @@ def push_market_value():
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# 准备要推送的数据
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first_mean = predictdata_df['top_models_mean'].iloc[0]
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last_mean = predictdata_df['top_models_mean'].iloc[-1]
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# 保留两位小数
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first_mean = round(first_mean, 2)
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last_mean = round(last_mean, 2)
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predictdata = [
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{
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"dataItemNo": global_config['bdwd_items']['ciri'],
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"dataDate": global_config['end_time'],
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"dataDate": global_config['end_time'].replace('-',''),
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"dataStatus": "add",
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"dataValue": first_mean
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},
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{
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"dataItemNo": global_config['bdwd_items']['benzhou'],
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"dataDate": global_config['end_time'],
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"dataDate": global_config['end_time'].replace('-',''),
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"dataStatus": "add",
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"dataValue": last_mean
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}
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@ -107,29 +107,34 @@ def push_market_value():
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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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# 保留两位小数
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ciyue_mean = round(ciyue_mean, 2)
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cieryue_mean = round(cieryue_mean, 2)
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cisanyue_mean = round(cisanyue_mean, 2)
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cisieryue_mean = round(cisieryue_mean, 2)
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predictdata = [
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{
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"dataItemNo": global_config['bdwd_items']['ciyue'],
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"dataDate": global_config['end_time'],
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"dataDate": global_config['end_time'].replace('-',''),
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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": global_config['end_time'],
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"dataDate": global_config['end_time'].replace('-',''),
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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": global_config['end_time'],
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"dataDate": global_config['end_time'].replace('-',''),
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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": global_config['end_time'],
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"dataDate": global_config['end_time'].replace('-',''),
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"dataStatus": "add",
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"dataValue": cisieryue_mean
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}
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@ -105,17 +105,20 @@ def push_market_value():
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# 准备要推送的数据
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first_mean = predictdata_df['top_models_mean'].iloc[0]
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last_mean = predictdata_df['top_models_mean'].iloc[-1]
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# 保留两位小数
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first_mean = round(first_mean, 2)
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last_mean = round(last_mean, 2)
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predictdata = [
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{
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"dataItemNo": global_config['bdwd_items']['cizhou'],
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"dataDate": global_config['end_time'],
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"dataDate": global_config['end_time'].replace('-',''),
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"dataStatus": "add",
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"dataValue": first_mean
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},
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{
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"dataItemNo": global_config['bdwd_items']['gezhou'],
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"dataDate": global_config['end_time'],
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"dataDate": global_config['end_time'].replace('-',''),
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"dataStatus": "add",
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"dataValue": last_mean
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}
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@ -165,7 +165,7 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
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# VanillaTransformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
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# Autoformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
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NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),
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# NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),
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# NBEATSx (h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',activation='ReLU', ), //报错
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# HINT(h=horizon),
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