聚烯烃图片报告标题更改
This commit is contained in:
workpc
parent
63de7af71d
commit
ae2dba7981
@ -380,7 +380,7 @@ DEFAULT_CONFIG = {
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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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@ -143,165 +143,23 @@ ClassifyId = 1161
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# 变量定义--线上环境
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# server_host = '10.200.32.39'
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# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
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# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
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# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
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# query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/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}/jingbo-api/api/dw/dataValue/pushDataValueList"
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# # 上传停更数据到市场信息平台
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# push_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
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# # 获取预警数据中取消订阅指标ID
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# get_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/dataList"
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# login_data = {
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# "data": {
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# "account": "api_dev",
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# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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# "terminal": "API"
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# },
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# "funcModule": "API",
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# "funcOperation": "获取token"
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# }
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# upload_data = {
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# "funcModule": '研究报告信息',
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# "funcOperation": '上传聚烯烃PP价格预测报告',
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# "data": {
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# "groupNo": '000211', # 用户组编号
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# "ownerAccount": '36541', # 报告所属用户账号 36541 - 贾青雪
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# "reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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# "fileName": '', # 文件名称
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# "fileBase64": '', # 文件内容base64
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# "categoryNo": 'jxtjgycbg', # 研究报告分类编码
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# "smartBusinessClassCode": 'JXTJGYCBG', # 分析报告分类编码
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# "reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
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# "reportDeptCode": "JXTJGYCBG", # 报告部门 - 002000621000 SH期货研究部
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# "productGroupCode": "RAW_MATERIAL" # 商品分类
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# }
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# }
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# warning_data = {
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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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# "groupNo": "000211",
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# 'WARNING_TYPE_NAME': '特征数据停更预警',
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# 'WARNING_CONTENT': '',
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# 'WARNING_DATE': ''
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# }
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# }
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# query_data_list_item_nos_data = {
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# "funcModule": "数据项",
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# "funcOperation": "查询",
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# "data": {
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# "dateStart": "20200101",
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# "dateEnd": "",
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# # 数据项编码,代表 PP期货 价格
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# "dataItemNoList": ["MAIN_CONFT_SETTLE_PRICE"]
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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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# push_waring_data_value_list_data = {
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# "data": {
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# "crudeOilWarningDtoList": [
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# {
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# "lastUpdateDate": "20240501",
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# "updateSuspensionCycle": 1,
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# "dataSource": "9",
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# "frequency": "1",
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# "indicatorName": "美元指数",
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# "indicatorId": "myzs001",
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# "warningDate": "2024-05-13"
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# }
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# ],
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# "dataSource": "9"
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# },
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# "funcModule": "商品数据同步",
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# "funcOperation": "同步"
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# }
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# get_waring_data_value_list_data = {
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# "data": "9", "funcModule": "商品数据同步", "funcOperation": "同步"}
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# # 八大维度数据项编码
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# bdwd_items = {
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# 'ciri': '251889263|FORECAST|PRICE|T01',
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# 'cierri': '251889263|FORECAST|PRICE|T02',
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# 'cisanri': '251889263|FORECAST|PRICE|T03',
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# 'cisiri': '251889263|FORECAST|PRICE|T04',
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# 'benzhou': '251889263|FORECAST|PRICE|T05',
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# 'cizhou': '251889263|FORECAST|PRICE|W_01',
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# 'gezhou': '251889263|FORECAST|PRICE|W_02',
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# 'ciyue': '251889263|FORECAST|PRICE|M_01',
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# 'cieryue': '251889263|FORECAST|PRICE|M_02',
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# 'cisanyue': '251889263|FORECAST|PRICE|M_03',
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# 'cisiyue': '251889263|FORECAST|PRICE|M_04',
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# }
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# # 生产环境数据库
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# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
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# port = 3306
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# dbusername = 'jingbo'
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# password = 'shihua@123'
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# dbname = 'jingbo'
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# table_name = 'v_tbl_crude_oil_warning'
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# 变量定义--测试环境
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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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# 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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server_host = '10.200.32.39'
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login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
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upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
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upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
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query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/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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push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
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# 上传停更数据到市场信息平台
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push_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
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push_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
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# 获取预警数据中取消订阅指标ID
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get_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/dataList"
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get_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/dataList"
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login_data = {
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"data": {
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"account": "api_test",
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# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
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"account": "api_dev",
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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"terminal": "API"
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},
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@ -309,29 +167,29 @@ login_data = {
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"funcOperation": "获取token"
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}
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upload_data = {
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"funcModule": '研究报告信息',
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"funcOperation": '上传聚烯烃PP价格预测报告',
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"data": {
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"groupNo": "000127",
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"ownerAccount": 'arui', # 报告所属用户账号
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"groupNo": '000211', # 用户组编号
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"ownerAccount": '36541', # 报告所属用户账号 36541 - 贾青雪
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"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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"fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
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"fileName": '', # 文件名称
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"fileBase64": '', # 文件内容base64
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"categoryNo": 'yyjgycbg', # 研究报告分类编码
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"categoryNo": 'jxtjgycbg', # 研究报告分类编码
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"smartBusinessClassCode": 'JXTJGYCBG', # 分析报告分类编码
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"reportEmployeeCode": "E40116", # 报告人
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"reportDeptCode": "D0044", # 报告部门
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"reportEmployeeCode": "E40482", # 报告人 E40482 - 管理员 0000027663 - 刘小朋
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"reportDeptCode": "JXTJGYCBG", # 报告部门 - 002000621000 SH期货研究部
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"productGroupCode": "RAW_MATERIAL" # 商品分类
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}
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}
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# 已弃用
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warning_data = {
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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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"groupNo": "000127",
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"groupNo": "000211",
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'WARNING_TYPE_NAME': '特征数据停更预警',
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'WARNING_CONTENT': '',
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'WARNING_DATE': ''
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@ -399,18 +257,160 @@ get_waring_data_value_list_data = {
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# 八大维度数据项编码
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bdwd_items = {
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'ciri': 'jxtppbdwdcr',
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'cierri': 'jxtppbdwdcer',
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'cisanri': 'jxtppbdwdcsanr',
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'cisiri': 'jxtppbdwdcsir',
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'benzhou': 'jxtppbdwdbz',
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'cizhou': 'jxtppbdwdcz',
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'gezhou': 'jxtppbdwdgz',
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'ciyue': 'jxtppbdwdcy',
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'cieryue': 'jxtppbdwdcey',
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'cisanyue': 'jxtppbdwdcsany',
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'cisiyue': 'jxtppbdwdcsiy',
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'ciri': '251889263|FORECAST|PRICE|T01',
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'cierri': '251889263|FORECAST|PRICE|T02',
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'cisanri': '251889263|FORECAST|PRICE|T03',
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'cisiri': '251889263|FORECAST|PRICE|T04',
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'benzhou': '251889263|FORECAST|PRICE|T05',
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'cizhou': '251889263|FORECAST|PRICE|W_01',
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'gezhou': '251889263|FORECAST|PRICE|W_02',
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'ciyue': '251889263|FORECAST|PRICE|M_01',
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'cieryue': '251889263|FORECAST|PRICE|M_02',
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'cisanyue': '251889263|FORECAST|PRICE|M_03',
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'cisiyue': '251889263|FORECAST|PRICE|M_04',
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}
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# 生产环境数据库
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# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
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# port = 3306
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# dbusername = 'jingbo'
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# password = 'shihua@123'
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# dbname = 'jingbo'
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# table_name = 'v_tbl_crude_oil_warning'
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# 变量定义--测试环境
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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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# # 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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# # 上传停更数据到市场信息平台
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# push_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
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# # 获取预警数据中取消订阅指标ID
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# get_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/dataList"
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# login_data = {
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# "data": {
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# "account": "api_test",
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# # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
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# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
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# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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# "terminal": "API"
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# },
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# "funcModule": "API",
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# "funcOperation": "获取token"
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# }
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# upload_data = {
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# "funcModule": '研究报告信息',
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# "funcOperation": '上传聚烯烃PP价格预测报告',
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# "data": {
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# "groupNo": "000127",
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# "ownerAccount": 'arui', # 报告所属用户账号
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# "reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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# "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
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# "fileBase64": '', # 文件内容base64
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# "categoryNo": 'yyjgycbg', # 研究报告分类编码
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# "smartBusinessClassCode": 'JXTJGYCBG', # 分析报告分类编码
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# "reportEmployeeCode": "E40116", # 报告人
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# "reportDeptCode": "D0044", # 报告部门
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# "productGroupCode": "RAW_MATERIAL" # 商品分类
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# }
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# }
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# # 已弃用
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# warning_data = {
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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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# "groupNo": "000127",
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# 'WARNING_TYPE_NAME': '特征数据停更预警',
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# 'WARNING_CONTENT': '',
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# 'WARNING_DATE': ''
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# }
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# }
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# query_data_list_item_nos_data = {
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# "funcModule": "数据项",
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# "funcOperation": "查询",
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# "data": {
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# "dateStart": "20200101",
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# "dateEnd": "",
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# # 数据项编码,代表 PP期货 价格
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# "dataItemNoList": ["MAIN_CONFT_SETTLE_PRICE"]
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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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# },
|
||||
# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.55
|
||||
# },
|
||||
# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.55
|
||||
# }
|
||||
# ]
|
||||
# }
|
||||
|
||||
|
||||
# push_waring_data_value_list_data = {
|
||||
# "data": {
|
||||
# "crudeOilWarningDtoList": [
|
||||
# {
|
||||
# "lastUpdateDate": "20240501",
|
||||
# "updateSuspensionCycle": 1,
|
||||
# "dataSource": "9",
|
||||
# "frequency": "1",
|
||||
# "indicatorName": "美元指数",
|
||||
# "indicatorId": "myzs001",
|
||||
# "warningDate": "2024-05-13"
|
||||
# }
|
||||
# ],
|
||||
# "dataSource": "9"
|
||||
# },
|
||||
# "funcModule": "商品数据同步",
|
||||
# "funcOperation": "同步"
|
||||
# }
|
||||
|
||||
|
||||
# get_waring_data_value_list_data = {
|
||||
# "data": "9", "funcModule": "商品数据同步", "funcOperation": "同步"}
|
||||
|
||||
|
||||
# # 八大维度数据项编码
|
||||
# bdwd_items = {
|
||||
# 'ciri': 'jxtppbdwdcr',
|
||||
# 'cierri': 'jxtppbdwdcer',
|
||||
# 'cisanri': 'jxtppbdwdcsanr',
|
||||
# 'cisiri': 'jxtppbdwdcsir',
|
||||
# 'benzhou': 'jxtppbdwdbz',
|
||||
# 'cizhou': 'jxtppbdwdcz',
|
||||
# 'gezhou': 'jxtppbdwdgz',
|
||||
# 'ciyue': 'jxtppbdwdcy',
|
||||
# 'cieryue': 'jxtppbdwdcey',
|
||||
# 'cisanyue': 'jxtppbdwdcsany',
|
||||
# 'cisiyue': 'jxtppbdwdcsiy',
|
||||
# }
|
||||
# 北京环境数据库
|
||||
host = '192.168.101.27'
|
||||
port = 3306
|
||||
@ -437,7 +437,7 @@ DEFAULT_CONFIG = {
|
||||
# 开关
|
||||
is_train = True # 是否训练
|
||||
is_debug = False # 是否调试
|
||||
is_eta = False # 是否使用eta接口
|
||||
is_eta = True # 是否使用eta接口
|
||||
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
|
||||
is_timefurture = True # 是否使用时间特征
|
||||
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
|
||||
|
||||
@ -336,12 +336,12 @@ columnsrename = {'251889263|FORECAST|PRICE|T05': '本周', '251889263|FORECAST|P
|
||||
|
||||
|
||||
# 生产环境数据库
|
||||
# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
|
||||
# port = 3306
|
||||
# dbusername ='jingbo'
|
||||
# password = 'shihua@123'
|
||||
# dbname = 'jingbo'
|
||||
# table_name = 'v_tbl_crude_oil_warning'
|
||||
host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
|
||||
port = 3306
|
||||
dbusername ='jingbo'
|
||||
password = 'shihua@123'
|
||||
dbname = 'jingbo'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
# 变量定义--测试环境
|
||||
@ -460,12 +460,12 @@ columnsrename = {'251889263|FORECAST|PRICE|T05': '本周', '251889263|FORECAST|P
|
||||
# 'jxtppbdwdcsany': '次三月', 'jxtppbdwdcy': '次月', 'jxtppbdwdcz': '次周', 'jxtppbdwdgz': '隔周', }
|
||||
|
||||
# 北京环境数据库
|
||||
host = '192.168.101.27'
|
||||
port = 3306
|
||||
dbusername = 'root'
|
||||
password = '123456'
|
||||
dbname = 'jingbo_test'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
# host = '192.168.101.27'
|
||||
# port = 3306
|
||||
# dbusername = 'root'
|
||||
# password = '123456'
|
||||
# dbname = 'jingbo_test'
|
||||
# table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
'feature_factor_frequency': 'D',
|
||||
|
||||
@ -301,12 +301,12 @@ bdwd_items = {
|
||||
|
||||
|
||||
# # 生产环境数据库
|
||||
# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
|
||||
# port = 3306
|
||||
# dbusername = 'jingbo'
|
||||
# password = 'shihua@123'
|
||||
# dbname = 'jingbo'
|
||||
# table_name = 'v_tbl_crude_oil_warning'
|
||||
host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
|
||||
port = 3306
|
||||
dbusername = 'jingbo'
|
||||
password = 'shihua@123'
|
||||
dbname = 'jingbo'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
# 变量定义--测试环境
|
||||
@ -439,12 +439,12 @@ bdwd_items = {
|
||||
|
||||
|
||||
# 北京环境数据库
|
||||
host = '192.168.101.27'
|
||||
port = 3306
|
||||
dbusername = 'root'
|
||||
password = '123456'
|
||||
dbname = 'jingbo_test'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
# host = '192.168.101.27'
|
||||
# port = 3306
|
||||
# dbusername = 'root'
|
||||
# password = '123456'
|
||||
# dbname = 'jingbo_test'
|
||||
# table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
'feature_factor_frequency': 'D',
|
||||
|
||||
@ -3,6 +3,8 @@ from lib.dataread import *
|
||||
from config_juxiting_yuedu import *
|
||||
import datetime
|
||||
|
||||
from models.nerulforcastmodels import pp_bdwd_png
|
||||
|
||||
global_config.update({
|
||||
# 核心参数
|
||||
'logger': logger,
|
||||
@ -111,4 +113,12 @@ def push_png_report():
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 图片报告
|
||||
try:
|
||||
logger.info('图片报告ing')
|
||||
pp_bdwd_png(global_config=global_config)
|
||||
logger.info('图片报告end')
|
||||
except Exception as e:
|
||||
logger.info(f'图片报告失败：{e}')
|
||||
time.sleep(5)
|
||||
push_png_report()
|
||||
|
||||
@ -994,6 +994,9 @@ def datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_time='', y
|
||||
# kdj指标
|
||||
if add_kdj:
|
||||
df = calculate_kdj(df)
|
||||
# 保存填充后的数据
|
||||
df.to_csv(os.path.join(
|
||||
dataset, '处理后的特征数据.csv'), index=False)
|
||||
|
||||
# 特征分析
|
||||
featureAnalysis(df, dataset=dataset, y=y)
|
||||
@ -1080,7 +1083,6 @@ def zhoududatachuli(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_time
|
||||
|
||||
|
||||
def datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_time='', y='y', dataset='dataset', delweekenday=True, add_kdj=False, is_timefurture=False):
|
||||
|
||||
'''
|
||||
聚烯烃特征数据处理函数，
|
||||
接收的是两个df，一个是指标数据，一个是指标列表
|
||||
@ -1176,14 +1178,12 @@ def datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_t
|
||||
# df = df.dropna(subset=['y'])
|
||||
# config.logger.info(f'删除预测列为空值的行后数据量：{df.shape}')
|
||||
|
||||
|
||||
# 去掉指标列表中的columns_to_drop的行
|
||||
df_zhibiaoliebiao = df_zhibiaoliebiao[df_zhibiaoliebiao['指标名称'].isin(
|
||||
df.columns.tolist())]
|
||||
df_zhibiaoliebiao.to_csv(os.path.join(
|
||||
dataset, '特征处理后的指标名称及分类.csv'), index=False)
|
||||
|
||||
|
||||
if add_kdj:
|
||||
df = calculate_kdj(df)
|
||||
|
||||
@ -1334,7 +1334,6 @@ def getdata_juxiting(filename, datecol='date', y='y', dataset='', add_kdj=False,
|
||||
df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, datecol, y=y, dataset=dataset,
|
||||
add_kdj=add_kdj, is_timefurture=is_timefurture, end_time=end_time)
|
||||
|
||||
|
||||
return df, df_zhibiaoliebiao
|
||||
|
||||
|
||||
|
||||
52
lib/tools.py
52
lib/tools.py
@ -866,8 +866,6 @@ def find_best_models(date='', global_config=None):
|
||||
best_models = {}
|
||||
model_id_name_dict = get_model_id_name_dict(global_config=global_config)
|
||||
|
||||
|
||||
|
||||
# 处理日期输入
|
||||
if not date:
|
||||
date = datetime.datetime.now().strftime('%Y-%m-%d')
|
||||
@ -881,7 +879,6 @@ def find_best_models(date='', global_config=None):
|
||||
return best_models
|
||||
current_date = datetime.datetime.strptime(date, '%Y-%m-%d')
|
||||
|
||||
|
||||
# 上一交易日日期 Last trading day
|
||||
last_trading_day = pd.Timestamp(date) - pd.tseries.offsets.BusinessDay(1)
|
||||
last_trading_day_str = last_trading_day.strftime('%Y-%m-%d')
|
||||
@ -978,7 +975,8 @@ def find_best_models(date='', global_config=None):
|
||||
if i == 0:
|
||||
# 计算当前日期的前一工作日日期
|
||||
ciridate = last_trading_day_str
|
||||
global_config['logger'].info(f'计算预测{last_trading_day}的次日{last_trading_day}最佳模型')
|
||||
global_config['logger'].info(
|
||||
f'计算预测{last_trading_day}的次日{last_trading_day}最佳模型')
|
||||
global_config['logger'].info(
|
||||
f'{date}真实价格：{true_price[true_price["ds"] == ciridate]["y"].values[0]}')
|
||||
price = df[['data_date', wd, 'model_id']]
|
||||
@ -1006,7 +1004,8 @@ def find_best_models(date='', global_config=None):
|
||||
# 计算五个工作日之前的日期
|
||||
benzhoudate = (pd.Timestamp(last_trading_day) -
|
||||
pd.Timedelta(days=7)).strftime('%Y-%m-%d')
|
||||
global_config['logger'].info(f'计算预测{last_trading_day}的五天前{benzhoudate}最佳模型')
|
||||
global_config['logger'].info(
|
||||
f'计算预测{last_trading_day}的五天前{benzhoudate}最佳模型')
|
||||
global_config['logger'].info(
|
||||
f'{date}真实价格：{true_price[true_price["ds"] == last_trading_day_str]["y"].values[0]}')
|
||||
price = df[['data_date', wd, 'model_id']]
|
||||
@ -1033,12 +1032,14 @@ def find_best_models(date='', global_config=None):
|
||||
elif i in [2, 3]:
|
||||
weeks_ago = 1 if i == 2 else 2
|
||||
ago_monday = last_trading_day - \
|
||||
datetime.timedelta(days=last_trading_day.weekday() + 7 * weeks_ago)
|
||||
datetime.timedelta(
|
||||
days=last_trading_day.weekday() + 7 * weeks_ago)
|
||||
ago_sunday = ago_monday + datetime.timedelta(days=6)
|
||||
ago_date_str = f"{ago_monday.strftime('%Y-%m-%d')} - {ago_sunday.strftime('%Y-%m-%d')}"
|
||||
global_config['logger'].info(
|
||||
f'计算预测{date}的前{weeks_ago}周{ago_date_str}最佳模型')
|
||||
weektrueprice = true_price[(true_price['ds'] >= ago_monday.strftime('%Y-%m-%d')) & (true_price['ds'] <= ago_sunday.strftime('%Y-%m-%d'))]['y'].mean()
|
||||
weektrueprice = true_price[(true_price['ds'] >= ago_monday.strftime(
|
||||
'%Y-%m-%d')) & (true_price['ds'] <= ago_sunday.strftime('%Y-%m-%d'))]['y'].mean()
|
||||
global_config['logger'].info(
|
||||
f'当周{date_monday.strftime("%Y-%m-%d")}---{last_trading_day_str}真实价格的周均价：{weektrueprice}')
|
||||
|
||||
@ -1102,7 +1103,7 @@ def find_best_models(date='', global_config=None):
|
||||
return best_models
|
||||
|
||||
|
||||
def plot_pp_predict_result(y_hat, global_config,wd='yuedu'):
|
||||
def plot_pp_predict_result(y_hat, global_config, wd='yuedu'):
|
||||
"""
|
||||
绘制PP期货预测结果的图表
|
||||
"""
|
||||
@ -1115,11 +1116,16 @@ def plot_pp_predict_result(y_hat, global_config,wd='yuedu'):
|
||||
global_config['dataset'], '指标数据.csv'))[['ds', 'y']][-12:]
|
||||
xgx_df = pd.read_csv(os.path.join(
|
||||
global_config['dataset'], '相关系数.csv'))
|
||||
title = 'PP期货月维度预测价格走势'
|
||||
|
||||
else:
|
||||
y = pd.read_csv('juxitingdataset/指标数据.csv')[['ds', 'y']][-30:]
|
||||
xgx_df = pd.read_csv('juxitingdataset/相关系数.csv')
|
||||
xgx_df = xgx_df.rename(columns={xgx_df.columns[0]: '指标', xgx_df.columns[1]: '系数'})
|
||||
top_10_correlations = xgx_df.sort_values(by='系数', ascending=False)[1:11].round(2)
|
||||
title = 'PP期货日、周维度预测价格走势'
|
||||
xgx_df = xgx_df.rename(
|
||||
columns={xgx_df.columns[0]: '指标', xgx_df.columns[1]: '系数'})
|
||||
top_10_correlations = xgx_df.sort_values(
|
||||
by='系数', ascending=False)[1:11].round(2)
|
||||
y['ds'] = pd.to_datetime(y['ds'])
|
||||
y = y[y['ds'] < y_hat['ds'].iloc[0]]
|
||||
|
||||
@ -1148,7 +1154,6 @@ def plot_pp_predict_result(y_hat, global_config,wd='yuedu'):
|
||||
sns.scatterplot(x=y_y_hat['ds'], y=y_y_hat['predictresult'],
|
||||
color='orange', ax=ax, marker='o', s=100)
|
||||
|
||||
|
||||
# 绘制 y 的折线图，颜色为蓝色
|
||||
sns.lineplot(x=y['ds'], y=y['y'], color='blue', label='真实值', ax=ax)
|
||||
# 月度日期每月显示一个
|
||||
@ -1158,7 +1163,9 @@ def plot_pp_predict_result(y_hat, global_config,wd='yuedu'):
|
||||
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
|
||||
|
||||
# date_str = pd.Timestamp(y_hat["ds"].iloc[0]).strftime('%Y-%m-%d')
|
||||
ax.set_title(f'{datetime.datetime.now().strftime("%Y-%m-%d")} PP期货十一大维度 预测价格走势', fontsize=24)
|
||||
ax.set_title(
|
||||
f'{datetime.datetime.now().strftime("%Y-%m-%d")} {title}', fontsize=24)
|
||||
|
||||
ax.set_xlabel('日期')
|
||||
ax.set_ylabel('预测结果')
|
||||
ax.tick_params(axis='x', rotation=45)
|
||||
@ -1181,7 +1188,7 @@ def plot_pp_predict_result(y_hat, global_config,wd='yuedu'):
|
||||
|
||||
# 在图表下方添加表格
|
||||
table = ax.table(cellText=data, colLabels=columns,
|
||||
loc='bottom', bbox=[0, -0.4, 1, 0.2],cellLoc='center')
|
||||
loc='bottom', bbox=[0, -0.4, 1, 0.2], cellLoc='center')
|
||||
# 设置表头为浅蓝色
|
||||
for (i, j), cell in table.get_celld().items():
|
||||
if i == 0: # 表头行
|
||||
@ -1215,6 +1222,7 @@ def plot_pp_predict_result(y_hat, global_config,wd='yuedu'):
|
||||
pad_inches=1.0 # 增加边距
|
||||
)
|
||||
|
||||
|
||||
def merge_images(image1_path, image2_path, output_path, direction='horizontal'):
|
||||
img1 = Image.open(image1_path).convert('RGBA') # 保留透明通道
|
||||
img2 = Image.open(image2_path).convert('RGBA')
|
||||
@ -1226,12 +1234,16 @@ def merge_images(image1_path, image2_path, output_path, direction='horizontal'):
|
||||
# 统一尺寸（横向拼接统一高度，纵向拼接统一宽度）
|
||||
if direction == 'horizontal':
|
||||
new_height = max(height1, height2)
|
||||
img1 = img1.resize((int(width1 * new_height / height1), new_height), Image.LANCZOS)
|
||||
img2 = img2.resize((int(width2 * new_height / height2), new_height), Image.LANCZOS)
|
||||
img1 = img1.resize(
|
||||
(int(width1 * new_height / height1), new_height), Image.LANCZOS)
|
||||
img2 = img2.resize(
|
||||
(int(width2 * new_height / height2), new_height), Image.LANCZOS)
|
||||
else:
|
||||
new_width = max(width1, width2)
|
||||
img1 = img1.resize((new_width, int(height1 * new_width / width1)), Image.LANCZOS)
|
||||
img2 = img2.resize((new_width, int(height2 * new_width / width2)), Image.LANCZOS)
|
||||
img1 = img1.resize(
|
||||
(new_width, int(height1 * new_width / width1)), Image.LANCZOS)
|
||||
img2 = img2.resize(
|
||||
(new_width, int(height2 * new_width / width2)), Image.LANCZOS)
|
||||
|
||||
# 重新获取调整后的尺寸
|
||||
width1, height1 = img1.size
|
||||
@ -1245,12 +1257,12 @@ def merge_images(image1_path, image2_path, output_path, direction='horizontal'):
|
||||
|
||||
# 粘贴图片
|
||||
new_img.paste(img1, (0, 0))
|
||||
new_img.paste(img2, (width1, 0) if direction == 'horizontal' else (0, height1))
|
||||
new_img.paste(img2, (width1, 0) if direction ==
|
||||
'horizontal' else (0, height1))
|
||||
|
||||
# 保存为PNG保留透明（如需JPG可转为RGB）
|
||||
new_img.save(output_path, 'PNG')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
print('This is a tool, not a script.')
|
||||
|
||||
|
||||
|
||||
@ -7,6 +7,7 @@ from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, p
|
||||
import datetime
|
||||
import torch
|
||||
torch.set_float32_matmul_precision("high")
|
||||
torch.set_num_threads(4)
|
||||
|
||||
global_config.update({
|
||||
# 核心参数
|
||||
@ -566,9 +567,10 @@ if __name__ == '__main__':
|
||||
# except Exception as e:
|
||||
# logger.info(f'预测失败：{e}')
|
||||
# continue
|
||||
# global_config['end_time'] = '2025-08-05'
|
||||
|
||||
# global_config['end_time'] = '2025-08-14'
|
||||
predict_main()
|
||||
|
||||
# global_config['end_time'] = '2025-08-12'
|
||||
# global_config['end_time'] = '2025-08-14'
|
||||
# push_market_value()
|
||||
# sql_inset_predict(global_config)
|
||||
|
||||
@ -7,6 +7,7 @@ from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, p
|
||||
import datetime
|
||||
import torch
|
||||
torch.set_float32_matmul_precision("high")
|
||||
torch.set_num_threads(4)
|
||||
|
||||
global_config.update({
|
||||
# 核心参数
|
||||
@ -510,13 +511,7 @@ def predict_main():
|
||||
|
||||
# logger.info('制作报告end')
|
||||
|
||||
# 图片报告
|
||||
try:
|
||||
logger.info('图片报告ing')
|
||||
pp_bdwd_png(global_config=global_config)
|
||||
logger.info('图片报告end')
|
||||
except Exception as e:
|
||||
logger.info(f'图片报告失败：{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)
|
||||
@ -552,7 +547,7 @@ if __name__ == '__main__':
|
||||
# logger.info(f'预测失败：{e}')
|
||||
# continue
|
||||
|
||||
# global_config['end_time'] = '2025-08-13'
|
||||
# global_config['end_time'] = '2025-08-14'
|
||||
predict_main()
|
||||
# push_market_value()
|
||||
# sql_inset_predict(global_config)
|
||||
|
||||
@ -7,6 +7,7 @@ from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, p
|
||||
import datetime
|
||||
import torch
|
||||
torch.set_float32_matmul_precision("high")
|
||||
torch.set_num_threads(4)
|
||||
|
||||
global_config.update({
|
||||
# 核心参数
|
||||
@ -520,7 +521,7 @@ if __name__ == '__main__':
|
||||
# continue
|
||||
|
||||
|
||||
# global_config['end_time'] = '2025-08-12'
|
||||
# global_config['end_time'] = '2025-08-14'
|
||||
predict_main()
|
||||
|
||||
|
||||
|
||||
@ -17,7 +17,8 @@ def run_predictions(target_date):
|
||||
# 依次执行每个脚本
|
||||
for script in scripts:
|
||||
# command = [r"C:\Users\Hello\.conda\envs\predict\python", script] # liuruipc
|
||||
command = [r"C:/Users/EDY/.conda/envs/priceforecast/python.exe", script] #168pc
|
||||
command = [r"D:/yuanyouyuce/envs/priceforecast/python", script] # 线上配置
|
||||
# command = [r"C:/Users/EDY/.conda/envs/priceforecast/python.exe", script] #168pc
|
||||
# command = [r"C:/Users/Hello/.conda/envs/jaigeyuce/python.exe", script] #yitijipc
|
||||
subprocess.run(command, check=True)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user