原油周度调试上传预测值到市场信息平台
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workpc
parent
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commit
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@ -39,7 +39,6 @@ edbnamelist = [
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]
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# eta自有数据指标编码
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modelsindex = {
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'NHITS': 'SELF0000001',
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@ -90,15 +89,18 @@ data = {
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ClassifyId = 1214
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############################################################################################################### 变量定义--测试环境
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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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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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# 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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# 停更预警
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upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
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# 查询数据项编码
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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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# 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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@ -176,6 +178,12 @@ push_data_value_list_data = {
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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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@ -188,7 +196,7 @@ dbname = 'jingbo_test'
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table_name = 'v_tbl_crude_oil_warning'
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### 开关
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# 开关
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is_train = False # 是否训练
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is_debug = True # 是否调试
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is_eta = True # 是否使用eta接口
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@ -205,9 +213,9 @@ is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不
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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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# 连接到数据库
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db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
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db_mysql = MySQLDB(host=host, user=dbusername,
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password=password, database=dbname)
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db_mysql.connect()
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print("数据库连接成功", host, dbname, dbusername)
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@ -222,7 +230,7 @@ add_kdj = False # 是否添加kdj指标
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if add_kdj and is_edbnamelist:
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edbnamelist = edbnamelist+['K', 'D', 'J']
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### 模型参数
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# 模型参数
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y = 'Brent连1合约价格' # 原油指标数据的目标变量 Brent连1合约价格 Brent活跃合约
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# y = 'Brent连1合约价格' # 原油指标数据的目标变量 Brent连1合约价格 Brent活跃合约
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horizon = 5 # 预测的步长
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@ -234,16 +242,16 @@ early_stop_patience_steps = 5 # 早停的耐心步数
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test_size = 100 # 测试集大小，定义100，后面使用的时候重新赋值
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val_size = test_size # 验证集大小,同测试集大小
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### 特征筛选用到的参数
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# 特征筛选用到的参数
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k = 100 # 特征筛选数量，如果是0或者值比特征数量大，代表全部特征
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corr_threshold = 0.6 # 相关性大于0.6的特征
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rote = 0.06 # 绘图上下界阈值
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### 计算准确率
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# 计算准确率
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weight_dict = [0.4, 0.15, 0.1, 0.1, 0.25] # 权重
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### 文件
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# 文件
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data_set = '原油指标数据.xlsx' # 数据集文件
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dataset = 'yuanyoudataset' # 数据集文件夹
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@ -260,7 +268,7 @@ reportname = f'Brent原油大模型预测--{end_time}.pdf' # 报告文件名
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reportname = reportname.replace(':', '-') # 替换冒号
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if end_time == '':
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end_time = now
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### 邮件配置
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# 邮件配置
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username = '1321340118@qq.com'
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passwd = 'wgczgyhtyyyyjghi'
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# recv=['liurui_test@163.com','52585119@qq.com']
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@ -273,7 +281,7 @@ file=os.path.join(dataset,'reportname')
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ssl = True
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### 日志配置
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# 日志配置
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# 创建日志目录（如果不存在）
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log_dir = 'logs'
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@ -285,8 +293,10 @@ logger = logging.getLogger('my_logger')
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logger.setLevel(logging.INFO)
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# 配置文件处理器，将日志记录到文件
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file_handler = logging.handlers.RotatingFileHandler(os.path.join(log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
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file_handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
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file_handler = logging.handlers.RotatingFileHandler(os.path.join(
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log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
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file_handler.setFormatter(logging.Formatter(
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'%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
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# 配置控制台处理器，将日志打印到控制台
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console_handler = logging.StreamHandler()
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@ -297,4 +307,3 @@ logger.addHandler(file_handler)
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logger.addHandler(console_handler)
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# logger.info('当前配置：'+settings)
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@ -96,6 +96,8 @@ 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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# 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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@ -146,6 +148,39 @@ 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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@ -168,6 +203,7 @@ 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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@ -65,12 +71,67 @@ global_config.update({
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'edbdatapushurl': edbdatapushurl,
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'edbdeleteurl': edbdeleteurl,
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'edbbusinessurl': edbbusinessurl,
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'ClassifyId': ClassifyId,
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'classifylisturl': classifylisturl,
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# 数据库配置
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'sqlitedb': sqlitedb,
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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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first_date = predictdata_df['ds'].iloc[0].replace('-', '')
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last_date = predictdata_df['ds'].iloc[-1].replace('-', '')
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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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predictdata = [
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{
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"dataItemNo": global_config['bdwd_items']['cizhou'],
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"dataDate": first_date,
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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": last_date,
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"dataStatus": "add",
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"dataValue": last_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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