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No commits in common. "f5286990fa6b84bc08351ce6b3722d401d7669f8" and "5191ff7c691bde6b4b2de6c2e298b1b117ffa0a7" have entirely different histories.
f5286990fa
...
5191ff7c69
@ -18,10 +18,22 @@
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"import numpy as np\n",
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"# 变量定义\n",
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"login_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
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"search_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos\"\n",
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"# query_data_list_item_nos_url\n",
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"search_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos\" #jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos\n",
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"upload_url = \"http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList\"\n",
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"\n",
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"\n",
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"query_data_list_item_nos_data = {\n",
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" \"funcModule\": \"数据项\",\n",
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" \"funcOperation\": \"查询\",\n",
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" \"data\": {\n",
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" \"dateStart\": \"20200101\",\n",
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" \"dateEnd\": \"20241231\",\n",
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" \"dataItemNoList\": [\"Brentzdj\", \"Brentzgj\"] # 数据项编码,代表 brent最低价和最高价\n",
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" }\n",
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"}\n",
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"\n",
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"\n",
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"login_data = {\n",
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" \"data\": {\n",
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" \"account\": \"api_dev\",\n",
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@ -844,7 +856,7 @@
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" # headers1 = {\"Authorization\": token_push}\n",
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" # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))\n",
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" \n",
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" \n",
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"\n",
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"\n",
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"\n",
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"\n",
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@ -960,8 +972,6 @@
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" # 保存新的xls文件\n",
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" new_workbook.save(\"定性模型数据项12-11.xlsx\")\n",
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"\n",
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"\n",
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"\n",
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"\n"
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]
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},
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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BIN
aisenzhecode/液化石油气/化工品价格预测准确率.xlsx
Normal file
BIN
aisenzhecode/液化石油气/化工品价格预测准确率.xlsx
Normal file
Binary file not shown.
@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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320
config_shiyoujiao.py
Normal file
320
config_shiyoujiao.py
Normal file
@ -0,0 +1,320 @@
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import logging
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import os
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import logging.handlers
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import datetime
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from lib.tools import MySQLDB,SQLiteHandler
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# eta 接口token
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APPID = "XNLDvxZHHugj7wJ7"
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SECRET = "iSeU4s6cKKBVbt94htVY1p0sqUMqb2xa"
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# eta 接口url
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sourcelisturl = 'http://10.189.2.78:8108/v1/edb/source/list'
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classifylisturl = 'http://10.189.2.78:8108/v1/edb/classify/list?ClassifyType='
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uniquecodedataurl = 'http://10.189.2.78:8108/v1/edb/data?UniqueCode=4991c37becba464609b409909fe4d992&StartDate=2024-02-01'
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classifyidlisturl = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId='
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edbcodedataurl = 'http://10.189.2.78:8108/v1/edb/data?EdbCode='
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edbdatapushurl = 'http://10.189.2.78:8108/v1/edb/push'
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edbdeleteurl = 'http://10.189.2.78:8108/v1/edb/business/edb/del'
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edbbusinessurl = 'http://10.189.2.78:8108/v1/edb/business/data/del'
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edbcodelist = ['ID01385938','lmcads03 lme comdty',
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'GC1 COMB Comdty',
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'C2404171822',
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'dxy curncy',
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'S5443199 ',
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'S5479800',
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'S5443108',
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'H7358586',
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'LC3FM1 INDEX',
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'CNY REGN Curncy',
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's0105897',
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'M0067419',
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'M0066351',
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'S0266372',
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'S0266438',
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'S0266506',
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'ID01384463']
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# 临时写死用指定的列,与上面的edbcode对应，后面更改
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edbnamelist = [
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'ds','y',
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'LME铜价',
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'黄金连1合约',
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'Brent-WTI',
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'美元指数',
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'甲醇鲁南价格',
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'甲醇太仓港口价格',
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'山东丙烯主流价',
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'丙烷（山东）',
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'FEI丙烷 M1',
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'在岸人民币汇率',
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'南华工业品指数',
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'PVC期货主力',
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'PE期货收盘价',
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'PP连续-1月',
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'PP连续-5月',
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'PP连续-9月',
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'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）'
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]
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edbcodenamedict = {
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'ID01385938':'PP：拉丝：1102K：市场价：青州：国家能源宁煤（日）',
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'ID01384463':'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）',
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'lmcads03 lme comdty':'LME铜价',
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'GC1 COMB Comdty':'黄金连1合约',
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'C2404171822':'Brent-WTI',
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'dxy curncy':'美元指数',
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'S5443199 ':'甲醇鲁南价格',
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'S5479800':'甲醇太仓港口价格',
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'S5443108':'山东丙烯主流价',
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'H7358586':'丙烷（山东）',
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'LC3FM1 INDEX':'FEI丙烷 M1',
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'CNY REGN Curncy':'在岸人民币汇率',
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's0105897':'南华工业品指数',
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'M0067419':'PVC期货主力',
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'M0066351':'PE期货收盘价',
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'S0266372':'PP连续-1月',
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'S0266438':'PP连续-5月',
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'S0266506':'PP连续-9月',
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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': 'SELF0000077',
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'Informer':'SELF0000078',
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'LSTM':'SELF0000079',
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'iTransformer':'SELF0000080',
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'TSMixer':'SELF0000081',
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'TSMixerx':'SELF0000082',
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'PatchTST':'SELF0000083',
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'RNN':'SELF0000084',
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'GRU':'SELF0000085',
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'TCN':'SELF0000086',
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'BiTCN':'SELF0000087',
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'DilatedRNN':'SELF0000088',
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'MLP':'SELF0000089',
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'DLinear':'SELF0000090',
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'NLinear':'SELF0000091',
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'TFT':'SELF0000092',
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'FEDformer':'SELF0000093',
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'StemGNN':'SELF0000094',
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'MLPMultivariate':'SELF0000095',
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'TiDE':'SELF0000096',
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'DeepNPTS':'SELF0000097'
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}
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# eta 上传预测结果的请求体，后面发起请求的时候更改 model datalist 数据
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data = {
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"IndexCode": "",
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"IndexName": "价格预测模型",
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"Unit": "无",
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"Frequency": "日度",
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"SourceName": f"价格预测",
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"Remark": 'ddd',
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"DataList": [
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{
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"Date": "2024-05-02",
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"Value": 333444
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}
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]
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}
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# eta 分类
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# level：3才可以获取到数据，所以需要人工把能源化工下所有的level3级都找到
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# url = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId=1214'
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#ParentId ":1160, 能源化工
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# ClassifyId ":1214,原油 3912 石油焦
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#ParentId ":1214,"，就是原油下所有的数据。
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ClassifyId = 3707
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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"
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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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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":'000128', # 用户组编号
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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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warning_data = {
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"groupNo":'000128', # 用户组编号
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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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'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":"20241231",
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"dataItemNoList":["Brentzdj","Brentzgj"] # 数据项编码,代表 brent最低价和最高价
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}
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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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port = 3306
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dbusername ='root'
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password = '123456'
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dbname = 'jingbo_test'
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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_debug = True # 是否调试
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is_eta = True # 是否使用eta接口
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is_market = False # 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 = 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_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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# 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.connect()
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print("数据库连接成功",host,dbname,dbusername)
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|
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# 数据截取日期
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start_year = 2020 # 数据开始年份
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end_time = '' # 数据截取日期
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freq = 'B' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
|
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delweekenday = True if freq == 'B' else False # 是否删除周末数据
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is_corr = False # 特征是否参与滞后领先提升相关系数
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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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y = 'AVG-金能大唐久泰青州'
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avg_cols = [
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'PP：拉丝：1102K：出厂价：青州：国家能源宁煤（日）',
|
||||
'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）',
|
||||
'PP：拉丝：L5E89：出厂价：河北、鲁北：大唐内蒙多伦（日）',
|
||||
'PP：拉丝：HP550J：市场价：青岛：金能化学（日）'
|
||||
]
|
||||
offsite = 80
|
||||
offsite_col = ['PP：拉丝：HP550J：市场价：青岛：金能化学（日）']
|
||||
horizon =5 # 预测的步长
|
||||
input_size = 40 # 输入序列长度
|
||||
train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数
|
||||
val_check_steps = 30 # 评估频率
|
||||
early_stop_patience_steps = 5 # 早停的耐心步数
|
||||
# --- 交叉验证用的参数
|
||||
test_size = 200 # 测试集大小，定义100，后面使用的时候重新赋值
|
||||
val_size = test_size # 验证集大小,同测试集大小
|
||||
|
||||
### 特征筛选用到的参数
|
||||
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
|
||||
corr_threshold = 0.6 # 相关性大于0.6的特征
|
||||
rote = 0.06 # 绘图上下界阈值
|
||||
|
||||
### 计算准确率
|
||||
weight_dict = [0.4,0.15,0.1,0.1,0.25] # 权重
|
||||
|
||||
|
||||
### 文件
|
||||
data_set = '石油焦指标数据.xlsx' # 数据集文件
|
||||
dataset = 'shiyoujiaodataset' # 数据集文件夹
|
||||
|
||||
# 数据库名称
|
||||
db_name = os.path.join(dataset,'jbsh_juxiting.db')
|
||||
sqlitedb = SQLiteHandler(db_name)
|
||||
sqlitedb.connect()
|
||||
|
||||
settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
|
||||
# 获取日期时间
|
||||
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # 获取当前日期时间
|
||||
now = datetime.datetime.now().strftime('%Y-%m-%d') # 获取当前日期时间
|
||||
reportname = f'PP大模型预测报告--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
if end_time == '':
|
||||
end_time = now
|
||||
### 邮件配置
|
||||
username='1321340118@qq.com'
|
||||
passwd='wgczgyhtyyyyjghi'
|
||||
# recv=['liurui_test@163.com','52585119@qq.com']
|
||||
recv=['liurui_test@163.com']
|
||||
# recv=['liurui_test@163.com']
|
||||
title='reportname'
|
||||
content=y+'预测报告请看附件'
|
||||
file=os.path.join(dataset,'reportname')
|
||||
# file=os.path.join(dataset,'14-7-50--100-原油指标数据.xlsx-Brent连1合约价格--20240731175936-预测报告.pdf')
|
||||
ssl=True
|
||||
|
||||
|
||||
### 日志配置
|
||||
|
||||
# 创建日志目录（如果不存在）
|
||||
log_dir = 'logs'
|
||||
if not os.path.exists(log_dir):
|
||||
os.makedirs(log_dir)
|
||||
|
||||
# 配置日志记录器
|
||||
logger = logging.getLogger('my_logger')
|
||||
logger.setLevel(logging.INFO)
|
||||
|
||||
# 配置文件处理器，将日志记录到文件
|
||||
file_handler = logging.handlers.RotatingFileHandler(os.path.join(log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
|
||||
file_handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
|
||||
|
||||
# 配置控制台处理器，将日志打印到控制台
|
||||
console_handler = logging.StreamHandler()
|
||||
console_handler.setFormatter(logging.Formatter('%(message)s'))
|
||||
|
||||
# 将处理器添加到日志记录器
|
||||
logger.addHandler(file_handler)
|
||||
logger.addHandler(console_handler)
|
||||
|
||||
# logger.info('当前配置：'+settings)
|
||||
|
||||
@ -143,7 +143,7 @@ modelsindex = {
|
||||
}
|
||||
|
||||
# 百川数据指标编码
|
||||
baicangidnamedict = {
|
||||
baichuanidnamedict = {
|
||||
'1588348470396480000': '石油焦滨州-友泰',
|
||||
'1588348470396480000.00': '石油焦东营-海科瑞林',
|
||||
'1588348470396480000.00': '石油焦东营-华联2',
|
||||
@ -160,6 +160,8 @@ baicangidnamedict = {
|
||||
}
|
||||
|
||||
|
||||
# baichuanidnamedict = {'1588348470396475286': 'test1', '1666': 'test2'} # 北京环境测试用
|
||||
|
||||
# eta 上传预测结果的请求体，后面发起请求的时候更改 model datalist 数据
|
||||
data = {
|
||||
"IndexCode": "",
|
||||
@ -272,14 +274,14 @@ push_data_value_list_data = {
|
||||
}
|
||||
# 八大维度数据项编码
|
||||
bdwd_items = {
|
||||
# 'ciri': 'yyycbdwdcr',
|
||||
# 'benzhou': 'yyycbdwdbz',
|
||||
# 'cizhou': 'yyycbdwdcz',
|
||||
# 'gezhou': 'yyycbdwdgz',
|
||||
# 'ciyue': 'yyycbdwdcy',
|
||||
# 'cieryue': 'yyycbdwdcey',
|
||||
# 'cisanyue': 'yyycbdwdcsy',
|
||||
# 'cisiyue': 'yyycbdwdcsiy',
|
||||
'ciri': 'syjlyycbdwdcr',
|
||||
'benzhou': 'syjlyycbdwdbz',
|
||||
'cizhou': 'syjlyycbdwdcz',
|
||||
'gezhou': 'syjlyycbdwdgz',
|
||||
'ciyue': 'syjlyycbdwdcy',
|
||||
'cieryue': 'syjlyycbdwdcey',
|
||||
'cisanyue': 'syjlyycbdwdcsy',
|
||||
'cisiyue': 'syjlyycbdwdcsiy',
|
||||
}
|
||||
|
||||
# 北京环境数据库
|
||||
@ -326,7 +328,7 @@ if add_kdj and is_edbnamelist:
|
||||
edbnamelist = edbnamelist+['K', 'D', 'J']
|
||||
|
||||
# 模型参数
|
||||
y = 'B46cc7d0a90155b5bfd'
|
||||
y = '煅烧焦山东高硫(高端S < 3.5，普货)(元/吨)'
|
||||
avg_cols = [
|
||||
|
||||
]
|
||||
|
||||
@ -57,6 +57,7 @@ global_config = {
|
||||
'y': None, # 目标变量列名
|
||||
'is_fivemodels': None,
|
||||
'weight_dict': None,
|
||||
'baicangidnamedict': None, # 百川id名称映射
|
||||
|
||||
# 模型参数
|
||||
'data_set': None, # 数据集名称
|
||||
@ -120,6 +121,8 @@ global_config = {
|
||||
|
||||
# 数据库配置
|
||||
'sqlitedb': None,
|
||||
'db_mysql': None,
|
||||
'baichuan_table_name': None,
|
||||
}
|
||||
# 定义函数
|
||||
|
||||
@ -1199,6 +1202,8 @@ class Config:
|
||||
# 数据库配置
|
||||
@property
|
||||
def sqlitedb(self): return global_config['sqlitedb']
|
||||
@property
|
||||
def db_mysql(self): return global_config['db_mysql']
|
||||
|
||||
|
||||
config = Config()
|
||||
@ -2213,3 +2218,38 @@ def addtimecharacteristics(df, dataset):
|
||||
df.drop(columns=['quarter_start', 'quarter'], inplace=True)
|
||||
df.to_csv(os.path.join(dataset, '指标数据添加时间特征.csv'), index=False)
|
||||
return df
|
||||
|
||||
|
||||
# 从数据库获取百川数据，接收一个百川id列表，返回df格式的数据
|
||||
def get_baichuan_data(baichuanidnamedict):
|
||||
baichuanidlist = list(baichuanidnamedict.keys())
|
||||
# 连接数据库
|
||||
db = config.db_mysql
|
||||
db.connect()
|
||||
# 执行SQL查询 select BAICHUAN_ID,DATA_DATE,DATA_VALUE from V_TBL_BAICHUAN_YINGFU_VALUE where BAICHUAN_ID in ('1588348470396475286','1666');
|
||||
sql = f"SELECT BAICHUAN_ID,DATA_DATE,DATA_VALUE FROM {global_config['baichuan_table_name']} WHERE BAICHUAN_ID in ({','.join(baichuanidlist)})"
|
||||
|
||||
# 获取查询结果
|
||||
results = db.execute_query(sql)
|
||||
df = pd.DataFrame(results, columns=[
|
||||
'BAICHUAN_ID', 'DATA_DATE', 'DATA_VALUE'])
|
||||
|
||||
# 按BAICHUAN_ID 进行分组，然后按DATA_DATE合并
|
||||
df1 = pd.DataFrame(columns=['DATA_DATE'])
|
||||
for baichuan_id, group in df.groupby('BAICHUAN_ID'):
|
||||
# group 删除BAICHUAN_ID列
|
||||
group.drop(columns=['BAICHUAN_ID'], inplace=True)
|
||||
# group DATA_value 转换为float类型,保留两位小数
|
||||
group['DATA_VALUE'] = group['DATA_VALUE'].astype(float).round(2)
|
||||
# group 更改列名
|
||||
group.rename(
|
||||
columns={'DATA_VALUE': baichuanidnamedict[baichuan_id]}, inplace=True)
|
||||
# 按DATA_DATE合并
|
||||
df1 = pd.merge(
|
||||
df1, group[['DATA_DATE', baichuanidnamedict[baichuan_id]]], on='DATA_DATE', how='outer')
|
||||
# 把DATA_DATE 列转换成日期格式
|
||||
df1['date'] = pd.to_datetime(
|
||||
df1['DATA_DATE']).dt.strftime('%Y-%m-%d')
|
||||
df1.drop(columns=['DATA_DATE'], inplace=True)
|
||||
|
||||
return df1
|
||||
|
||||
301
main_shiyoujiao.py
Normal file
301
main_shiyoujiao.py
Normal file
@ -0,0 +1,301 @@
|
||||
# 读取配置
|
||||
from lib.dataread import *
|
||||
from lib.tools import SendMail,exception_logger
|
||||
from models.nerulforcastmodels import ex_Model_Juxiting,model_losss,model_losss_juxiting,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting
|
||||
|
||||
import glob
|
||||
import torch
|
||||
torch.set_float32_matmul_precision("high")
|
||||
|
||||
|
||||
|
||||
def predict_main():
|
||||
"""
|
||||
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。
|
||||
|
||||
参数:
|
||||
signature (BinanceAPI): Binance API 实例。
|
||||
etadata (EtaReader): ETA 数据读取器实例。
|
||||
is_eta (bool): 是否从 ETA 获取数据。
|
||||
data_set (str): 数据集名称。
|
||||
dataset (str): 数据集路径。
|
||||
add_kdj (bool): 是否添加 KDJ 指标。
|
||||
is_timefurture (bool): 是否添加时间衍生特征。
|
||||
end_time (str): 结束时间。
|
||||
is_edbnamelist (bool): 是否使用 EDB 名称列表。
|
||||
edbnamelist (list): EDB 名称列表。
|
||||
y (str): 预测目标列名。
|
||||
sqlitedb (SQLiteDB): SQLite 数据库实例。
|
||||
is_corr (bool): 是否进行相关性分析。
|
||||
horizon (int): 预测时域。
|
||||
input_size (int): 输入数据大小。
|
||||
train_steps (int): 训练步数。
|
||||
val_check_steps (int): 验证检查步数。
|
||||
early_stop_patience_steps (int): 早停耐心步数。
|
||||
is_debug (bool): 是否调试模式。
|
||||
dataset (str): 数据集名称。
|
||||
is_train (bool): 是否训练模型。
|
||||
is_fivemodels (bool): 是否使用五个模型。
|
||||
val_size (float): 验证集大小。
|
||||
test_size (float): 测试集大小。
|
||||
settings (dict): 模型设置。
|
||||
now (str): 当前时间。
|
||||
etadata (EtaReader): ETA 数据读取器实例。
|
||||
modelsindex (list): 模型索引列表。
|
||||
data (str): 数据类型。
|
||||
is_eta (bool): 是否从 ETA 获取数据。
|
||||
|
||||
返回:
|
||||
None
|
||||
"""
|
||||
global end_time
|
||||
signature = BinanceAPI(APPID, SECRET)
|
||||
etadata = EtaReader(signature=signature,
|
||||
classifylisturl=classifylisturl,
|
||||
classifyidlisturl=classifyidlisturl,
|
||||
edbcodedataurl=edbcodedataurl,
|
||||
edbcodelist=edbcodelist,
|
||||
edbdatapushurl=edbdatapushurl,
|
||||
edbdeleteurl=edbdeleteurl,
|
||||
edbbusinessurl=edbbusinessurl
|
||||
)
|
||||
# 获取数据
|
||||
if is_eta:
|
||||
logger.info('从eta获取数据...')
|
||||
signature = BinanceAPI(APPID, SECRET)
|
||||
etadata = EtaReader(signature=signature,
|
||||
classifylisturl=classifylisturl,
|
||||
classifyidlisturl=classifyidlisturl,
|
||||
edbcodedataurl=edbcodedataurl,
|
||||
edbcodelist=edbcodelist,
|
||||
edbdatapushurl=edbdatapushurl,
|
||||
edbdeleteurl=edbdeleteurl,
|
||||
edbbusinessurl=edbbusinessurl,
|
||||
)
|
||||
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_shiyoujiao_data(data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||
|
||||
if is_market:
|
||||
logger.info('从市场信息平台获取数据...')
|
||||
try:
|
||||
# 如果是测试环境，最高价最低价取excel文档
|
||||
if server_host == '192.168.100.53':
|
||||
logger.info('从excel文档获取最高价最低价')
|
||||
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||
else:
|
||||
logger.info('从市场信息平台获取数据')
|
||||
df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
|
||||
|
||||
except :
|
||||
logger.info('最高最低价拼接失败')
|
||||
|
||||
# 保存到xlsx文件的sheet表
|
||||
with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
|
||||
df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
|
||||
df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
||||
|
||||
|
||||
# 数据处理
|
||||
df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
||||
end_time=end_time)
|
||||
|
||||
else:
|
||||
# 读取数据
|
||||
logger.info('读取本地数据：' + os.path.join(dataset, data_set))
|
||||
df,df_zhibiaoliebiao = getdata_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
|
||||
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
||||
|
||||
# 更改预测列名称
|
||||
df.rename(columns={y: 'y'}, inplace=True)
|
||||
|
||||
if is_edbnamelist:
|
||||
df = df[edbnamelist]
|
||||
df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
|
||||
# 保存最新日期的y值到数据库
|
||||
# 取第一行数据存储到数据库中
|
||||
first_row = df[['ds', 'y']].tail(1)
|
||||
# 判断y的类型是否为float
|
||||
if not isinstance(first_row['y'].values[0], float):
|
||||
logger.info(f'{end_time}预测目标数据为空，跳过')
|
||||
return None
|
||||
|
||||
# 将最新真实值保存到数据库
|
||||
if not sqlitedb.check_table_exists('trueandpredict'):
|
||||
first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
|
||||
else:
|
||||
for row in first_row.itertuples(index=False):
|
||||
row_dict = row._asdict()
|
||||
row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
||||
check_query = sqlitedb.select_data('trueandpredict', where_condition=f"ds = '{row.ds}'")
|
||||
if len(check_query) > 0:
|
||||
set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||
sqlitedb.update_data('trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
||||
continue
|
||||
sqlitedb.insert_data('trueandpredict', tuple(row_dict.values()), columns=row_dict.keys())
|
||||
|
||||
# 更新accuracy表的y值
|
||||
if not sqlitedb.check_table_exists('accuracy'):
|
||||
pass
|
||||
else:
|
||||
update_y = sqlitedb.select_data('accuracy',where_condition="y is null")
|
||||
if len(update_y) > 0:
|
||||
logger.info('更新accuracy表的y值')
|
||||
# 找到update_y 中ds且df中的y的行
|
||||
update_y = update_y[update_y['ds']<=end_time]
|
||||
logger.info(f'要更新y的信息：{update_y}')
|
||||
# try:
|
||||
for row in update_y.itertuples(index=False):
|
||||
try:
|
||||
row_dict = row._asdict()
|
||||
yy = df[df['ds']==row_dict['ds']]['y'].values[0]
|
||||
LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0]
|
||||
HIGH = df[df['ds']==row_dict['ds']]['Brentzgj'].values[0]
|
||||
sqlitedb.update_data('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||
except:
|
||||
logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
||||
# except Exception as e:
|
||||
# logger.info(f'更新accuracy表的y值失败：{e}')
|
||||
|
||||
import datetime
|
||||
# 判断当前日期是不是周一
|
||||
is_weekday = datetime.datetime.now().weekday() == 0
|
||||
if is_weekday:
|
||||
logger.info('今天是周一，更新预测模型')
|
||||
# 计算最近60天预测残差最低的模型名称
|
||||
model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
|
||||
# 删除空值率为90%以上的列
|
||||
if len(model_results) > 10:
|
||||
model_results = model_results.dropna(thresh=len(model_results)*0.1,axis=1)
|
||||
# 删除空行
|
||||
model_results = model_results.dropna()
|
||||
modelnames = model_results.columns.to_list()[2:-1]
|
||||
for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||
model_results[col] = model_results[col].astype(np.float32)
|
||||
# 计算每个预测值与真实值之间的偏差率
|
||||
for model in modelnames:
|
||||
model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
|
||||
# 获取每行对应的最小偏差率值
|
||||
min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
# 获取每行对应的最小偏差率值对应的列名
|
||||
min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
# 将列名索引转换为列名
|
||||
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
|
||||
# 取出现次数最多的模型名称
|
||||
most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# 保存结果到数据库
|
||||
if not sqlitedb.check_table_exists('most_model'):
|
||||
sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
|
||||
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
|
||||
try:
|
||||
if is_weekday:
|
||||
# if True:
|
||||
logger.info('今天是周一，发送特征预警')
|
||||
# 上传预警信息到数据库
|
||||
warning_data_df = df_zhibiaoliebiao.copy()
|
||||
warning_data_df = warning_data_df[warning_data_df['停更周期']> 3 ][['指标名称', '指标id', '频度','更新周期','指标来源','最后更新时间','停更周期']]
|
||||
# 重命名列名
|
||||
warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
||||
from sqlalchemy import create_engine
|
||||
import urllib
|
||||
global password
|
||||
if '@' in password:
|
||||
password = urllib.parse.quote_plus(password)
|
||||
|
||||
engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
|
||||
warning_data_df['TENANT_CODE'] = 'T0004'
|
||||
# 插入数据之前查询表数据然后新增id列
|
||||
existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
||||
if not existing_data.empty:
|
||||
max_id = existing_data['ID'].astype(int).max()
|
||||
warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
|
||||
else:
|
||||
warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
||||
warning_data_df.to_sql(table_name, con=engine, if_exists='append', index=False)
|
||||
if is_update_warning_data:
|
||||
upload_warning_info(len(warning_data_df))
|
||||
except:
|
||||
logger.info('上传预警信息到数据库失败')
|
||||
|
||||
if is_corr:
|
||||
df = corr_feature(df=df)
|
||||
|
||||
df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
|
||||
logger.info(f"开始训练模型...")
|
||||
row, col = df.shape
|
||||
|
||||
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||
ex_Model_Juxiting(df,
|
||||
horizon=horizon,
|
||||
input_size=input_size,
|
||||
train_steps=train_steps,
|
||||
val_check_steps=val_check_steps,
|
||||
early_stop_patience_steps=early_stop_patience_steps,
|
||||
is_debug=is_debug,
|
||||
dataset=dataset,
|
||||
is_train=is_train,
|
||||
is_fivemodels=is_fivemodels,
|
||||
val_size=val_size,
|
||||
test_size=test_size,
|
||||
settings=settings,
|
||||
now=now,
|
||||
etadata=etadata,
|
||||
modelsindex=modelsindex,
|
||||
data=data,
|
||||
is_eta=is_eta,
|
||||
end_time=end_time,
|
||||
)
|
||||
|
||||
|
||||
logger.info('模型训练完成')
|
||||
|
||||
logger.info('训练数据绘图ing')
|
||||
model_results3 = model_losss_juxiting(sqlitedb)
|
||||
logger.info('训练数据绘图end')
|
||||
|
||||
# 模型报告
|
||||
logger.info('制作报告ing')
|
||||
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
reportname = f'PP大模型预测报告--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
pp_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
|
||||
reportname=reportname,sqlitedb=sqlitedb),
|
||||
|
||||
logger.info('制作报告end')
|
||||
logger.info('模型训练完成')
|
||||
|
||||
# # LSTM 单变量模型
|
||||
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
||||
|
||||
# # lstm 多变量模型
|
||||
# ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
|
||||
|
||||
# # GRU 模型
|
||||
# # ex_GRU(df)
|
||||
|
||||
# 发送邮件
|
||||
m = SendMail(
|
||||
username=username,
|
||||
passwd=passwd,
|
||||
recv=recv,
|
||||
title=title,
|
||||
content=content,
|
||||
file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
|
||||
ssl=ssl,
|
||||
)
|
||||
# m.send_mail()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# global end_time
|
||||
# is_on = True
|
||||
# # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
# for i_time in pd.date_range('2025-1-20', '2025-2-6', freq='B'):
|
||||
# end_time = i_time.strftime('%Y-%m-%d')
|
||||
# try:
|
||||
# predict_main()
|
||||
# except:
|
||||
# pass
|
||||
|
||||
predict_main()
|
||||
@ -3,7 +3,7 @@
|
||||
from lib.dataread import *
|
||||
from config_shiyoujiao_lvyong import *
|
||||
from lib.tools import SendMail, exception_logger
|
||||
from models.nerulforcastmodels import model_losss, shiyoujiao_lvyong_export_pdf
|
||||
from models.nerulforcastmodels import ex_Model, model_losss, model_losss_juxiting, brent_export_pdf, tansuanli_export_pdf, pp_export_pdf, model_losss_juxiting
|
||||
import datetime
|
||||
import torch
|
||||
torch.set_float32_matmul_precision("high")
|
||||
@ -18,6 +18,7 @@ global_config.update({
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'settings': settings,
|
||||
'weight_dict': weight_dict,
|
||||
'baichuanidnamedict': baichuanidnamedict,
|
||||
|
||||
|
||||
# 模型参数
|
||||
@ -72,11 +73,14 @@ global_config.update({
|
||||
'edbdatapushurl': edbdatapushurl,
|
||||
'edbdeleteurl': edbdeleteurl,
|
||||
'edbbusinessurl': edbbusinessurl,
|
||||
'edbcodenamedict': edbcodenamedict,
|
||||
'ClassifyId': ClassifyId,
|
||||
'classifylisturl': classifylisturl,
|
||||
|
||||
# 数据库配置
|
||||
'sqlitedb': sqlitedb,
|
||||
'db_mysql': db_mysql,
|
||||
'baichuan_table_name': baichuan_table_name,
|
||||
})
|
||||
|
||||
|
||||
@ -173,228 +177,242 @@ def predict_main():
|
||||
返回:
|
||||
None
|
||||
"""
|
||||
# end_time = global_config['end_time']
|
||||
# # 获取数据
|
||||
# if is_eta:
|
||||
# logger.info('从eta获取数据...')
|
||||
# signature = BinanceAPI(APPID, SECRET)
|
||||
# etadata = EtaReader(signature=signature,
|
||||
# classifylisturl=global_config['classifylisturl'],
|
||||
# classifyidlisturl=global_config['classifyidlisturl'],
|
||||
# edbcodedataurl=global_config['edbcodedataurl'],
|
||||
# edbcodelist=global_config['edbcodelist'],
|
||||
# edbdatapushurl=global_config['edbdatapushurl'],
|
||||
# edbdeleteurl=global_config['edbdeleteurl'],
|
||||
# edbbusinessurl=global_config['edbbusinessurl'],
|
||||
# classifyId=global_config['ClassifyId'],
|
||||
# )
|
||||
# df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_shiyoujiao_lvyong_data(
|
||||
# data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||
|
||||
# if is_market:
|
||||
# logger.info('从市场信息平台获取数据...')
|
||||
# try:
|
||||
# # 如果是测试环境，最高价最低价取excel文档
|
||||
# if server_host == '192.168.100.53':
|
||||
# logger.info('从excel文档获取最高价最低价')
|
||||
# df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||
# else:
|
||||
# logger.info('从市场信息平台获取数据')
|
||||
# df_zhibiaoshuju = get_market_data(
|
||||
# end_time, df_zhibiaoshuju)
|
||||
end_time = global_config['end_time']
|
||||
# 获取数据
|
||||
if is_eta:
|
||||
logger.info('从eta获取数据...')
|
||||
signature = BinanceAPI(APPID, SECRET)
|
||||
etadata = EtaReader(signature=signature,
|
||||
classifylisturl=global_config['classifylisturl'],
|
||||
classifyidlisturl=global_config['classifyidlisturl'],
|
||||
edbcodedataurl=global_config['edbcodedataurl'],
|
||||
edbcodelist=global_config['edbcodelist'],
|
||||
edbdatapushurl=global_config['edbdatapushurl'],
|
||||
edbdeleteurl=global_config['edbdeleteurl'],
|
||||
edbbusinessurl=global_config['edbbusinessurl'],
|
||||
classifyId=global_config['ClassifyId'],
|
||||
)
|
||||
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_shiyoujiao_lvyong_data(
|
||||
data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||
|
||||
# except:
|
||||
# logger.info('最高最低价拼接失败')
|
||||
if is_market:
|
||||
logger.info('从市场信息平台获取数据...')
|
||||
try:
|
||||
# 如果是测试环境，最高价最低价取excel文档
|
||||
if server_host == '192.168.100.53':
|
||||
logger.info('从excel文档获取最高价最低价')
|
||||
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||
else:
|
||||
logger.info('从市场信息平台获取数据')
|
||||
df_zhibiaoshuju = get_market_data(
|
||||
end_time, df_zhibiaoshuju)
|
||||
|
||||
# # 保存到xlsx文件的sheet表
|
||||
# with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
|
||||
# df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
|
||||
# df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
||||
except:
|
||||
logger.info('最高最低价拼接失败')
|
||||
|
||||
# # 数据处理
|
||||
# df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
||||
# end_time=end_time)
|
||||
if len(global_config['baichuanidnamedict']) > 0:
|
||||
logger.info('从市场数据库获取百川数据...')
|
||||
baichuandf = get_baichuan_data(global_config['baichuanidnamedict'])
|
||||
df_zhibiaoshuju = pd.merge(
|
||||
df_zhibiaoshuju, baichuandf, on='date', how='outer')
|
||||
# 指标列表添加百川数据
|
||||
df_baichuanliebiao = pd.DataFrame(
|
||||
global_config['baichuanidnamedict'].items(), columns=['指标id', '指标名称'])
|
||||
df_baichuanliebiao['指标分类'] = '百川'
|
||||
df_baichuanliebiao['频度'] = '其他'
|
||||
df_zhibiaoliebiao = pd.concat(
|
||||
[df_zhibiaoliebiao, df_baichuanliebiao], axis=0)
|
||||
|
||||
# else:
|
||||
# # 读取数据
|
||||
# logger.info('读取本地数据：' + os.path.join(dataset, data_set))
|
||||
# df, df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
|
||||
# is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
||||
# 保存到xlsx文件的sheet表
|
||||
with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
|
||||
df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
|
||||
df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
||||
|
||||
# # 更改预测列名称
|
||||
# df.rename(columns={y: 'y'}, inplace=True)
|
||||
# 数据处理
|
||||
df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
||||
end_time=end_time)
|
||||
|
||||
# if is_edbnamelist:
|
||||
# df = df[edbnamelist]
|
||||
# df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
|
||||
# # 保存最新日期的y值到数据库
|
||||
# # 取第一行数据存储到数据库中
|
||||
# first_row = df[['ds', 'y']].tail(1)
|
||||
# # 判断y的类型是否为float
|
||||
# if not isinstance(first_row['y'].values[0], float):
|
||||
# logger.info(f'{end_time}预测目标数据为空，跳过')
|
||||
# return None
|
||||
else:
|
||||
# 读取数据
|
||||
logger.info('读取本地数据：' + os.path.join(dataset, data_set))
|
||||
df, df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
|
||||
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
||||
|
||||
# # 将最新真实值保存到数据库
|
||||
# if not sqlitedb.check_table_exists('trueandpredict'):
|
||||
# first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
|
||||
# else:
|
||||
# for row in first_row.itertuples(index=False):
|
||||
# row_dict = row._asdict()
|
||||
# config.logger.info(f'要保存的真实值：{row_dict}')
|
||||
# # 判断ds是否为字符串类型,如果不是则转换为字符串类型
|
||||
# if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
|
||||
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||
# elif not isinstance(row_dict['ds'], str):
|
||||
# try:
|
||||
# row_dict['ds'] = pd.to_datetime(
|
||||
# row_dict['ds']).strftime('%Y-%m-%d')
|
||||
# except:
|
||||
# logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
|
||||
# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||
# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
||||
# check_query = sqlitedb.select_data(
|
||||
# 'trueandpredict', where_condition=f"ds = '{row.ds}'")
|
||||
# if len(check_query) > 0:
|
||||
# set_clause = ", ".join(
|
||||
# [f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||
# sqlitedb.update_data(
|
||||
# 'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
||||
# continue
|
||||
# sqlitedb.insert_data('trueandpredict', tuple(
|
||||
# row_dict.values()), columns=row_dict.keys())
|
||||
# 更改预测列名称
|
||||
df.rename(columns={y: 'y'}, inplace=True)
|
||||
|
||||
# # 更新accuracy表的y值
|
||||
# if not sqlitedb.check_table_exists('accuracy'):
|
||||
# pass
|
||||
# else:
|
||||
# update_y = sqlitedb.select_data(
|
||||
# 'accuracy', where_condition="y is null")
|
||||
# if len(update_y) > 0:
|
||||
# logger.info('更新accuracy表的y值')
|
||||
# # 找到update_y 中ds且df中的y的行
|
||||
# update_y = update_y[update_y['ds'] <= end_time]
|
||||
# logger.info(f'要更新y的信息：{update_y}')
|
||||
# # try:
|
||||
# for row in update_y.itertuples(index=False):
|
||||
# try:
|
||||
# row_dict = row._asdict()
|
||||
# yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
|
||||
# LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
|
||||
# HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
|
||||
# sqlitedb.update_data(
|
||||
# 'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||
# except:
|
||||
# logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
||||
# # except Exception as e:
|
||||
# # logger.info(f'更新accuracy表的y值失败：{e}')
|
||||
if is_edbnamelist:
|
||||
df = df[edbnamelist]
|
||||
df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
|
||||
# 保存最新日期的y值到数据库
|
||||
# 取第一行数据存储到数据库中
|
||||
first_row = df[['ds', 'y']].tail(1)
|
||||
# 判断y的类型是否为float
|
||||
if not isinstance(first_row['y'].values[0], float):
|
||||
logger.info(f'{end_time}预测目标数据为空，跳过')
|
||||
return None
|
||||
|
||||
# # 判断当前日期是不是周一
|
||||
# is_weekday = datetime.datetime.now().weekday() == 0
|
||||
# if is_weekday:
|
||||
# logger.info('今天是周一，更新预测模型')
|
||||
# # 计算最近60天预测残差最低的模型名称
|
||||
# model_results = sqlitedb.select_data(
|
||||
# 'trueandpredict', order_by="ds DESC", limit="60")
|
||||
# # 删除空值率为90%以上的列
|
||||
# if len(model_results) > 10:
|
||||
# model_results = model_results.dropna(
|
||||
# thresh=len(model_results)*0.1, axis=1)
|
||||
# # 删除空行
|
||||
# model_results = model_results.dropna()
|
||||
# modelnames = model_results.columns.to_list()[2:-1]
|
||||
# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||
# model_results[col] = model_results[col].astype(np.float32)
|
||||
# # 计算每个预测值与真实值之间的偏差率
|
||||
# for model in modelnames:
|
||||
# model_results[f'{model}_abs_error_rate'] = abs(
|
||||
# model_results['y'] - model_results[model]) / model_results['y']
|
||||
# # 获取每行对应的最小偏差率值
|
||||
# min_abs_error_rate_values = model_results.apply(
|
||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
# # 获取每行对应的最小偏差率值对应的列名
|
||||
# min_abs_error_rate_column_name = model_results.apply(
|
||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
# # 将列名索引转换为列名
|
||||
# min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
||||
# lambda x: x.split('_')[0])
|
||||
# # 取出现次数最多的模型名称
|
||||
# most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
# logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# # 保存结果到数据库
|
||||
# if not sqlitedb.check_table_exists('most_model'):
|
||||
# sqlitedb.create_table(
|
||||
# 'most_model', columns="ds datetime, most_common_model TEXT")
|
||||
# sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||
# '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
# 将最新真实值保存到数据库
|
||||
if not sqlitedb.check_table_exists('trueandpredict'):
|
||||
first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
|
||||
else:
|
||||
for row in first_row.itertuples(index=False):
|
||||
row_dict = row._asdict()
|
||||
config.logger.info(f'要保存的真实值：{row_dict}')
|
||||
# 判断ds是否为字符串类型,如果不是则转换为字符串类型
|
||||
if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
|
||||
row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||
elif not isinstance(row_dict['ds'], str):
|
||||
try:
|
||||
row_dict['ds'] = pd.to_datetime(
|
||||
row_dict['ds']).strftime('%Y-%m-%d')
|
||||
except:
|
||||
logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
|
||||
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
||||
check_query = sqlitedb.select_data(
|
||||
'trueandpredict', where_condition=f"ds = '{row.ds}'")
|
||||
if len(check_query) > 0:
|
||||
set_clause = ", ".join(
|
||||
[f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||
sqlitedb.update_data(
|
||||
'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
||||
continue
|
||||
sqlitedb.insert_data('trueandpredict', tuple(
|
||||
row_dict.values()), columns=row_dict.keys())
|
||||
|
||||
# try:
|
||||
# if is_weekday:
|
||||
# # if True:
|
||||
# logger.info('今天是周一，发送特征预警')
|
||||
# # 上传预警信息到数据库
|
||||
# warning_data_df = df_zhibiaoliebiao.copy()
|
||||
# warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
|
||||
# '指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
|
||||
# # 重命名列名
|
||||
# warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
|
||||
# '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
||||
# from sqlalchemy import create_engine
|
||||
# import urllib
|
||||
# global password
|
||||
# if '@' in password:
|
||||
# password = urllib.parse.quote_plus(password)
|
||||
# 更新accuracy表的y值
|
||||
if not sqlitedb.check_table_exists('accuracy'):
|
||||
pass
|
||||
else:
|
||||
update_y = sqlitedb.select_data(
|
||||
'accuracy', where_condition="y is null")
|
||||
if len(update_y) > 0:
|
||||
logger.info('更新accuracy表的y值')
|
||||
# 找到update_y 中ds且df中的y的行
|
||||
update_y = update_y[update_y['ds'] <= end_time]
|
||||
logger.info(f'要更新y的信息：{update_y}')
|
||||
# try:
|
||||
for row in update_y.itertuples(index=False):
|
||||
try:
|
||||
row_dict = row._asdict()
|
||||
yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
|
||||
LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
|
||||
HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
|
||||
sqlitedb.update_data(
|
||||
'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||
except:
|
||||
logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
||||
# except Exception as e:
|
||||
# logger.info(f'更新accuracy表的y值失败：{e}')
|
||||
|
||||
# engine = create_engine(
|
||||
# f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||
# warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
|
||||
# "%Y-%m-%d %H:%M:%S")
|
||||
# warning_data_df['TENANT_CODE'] = 'T0004'
|
||||
# # 插入数据之前查询表数据然后新增id列
|
||||
# existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
||||
# if not existing_data.empty:
|
||||
# max_id = existing_data['ID'].astype(int).max()
|
||||
# warning_data_df['ID'] = range(
|
||||
# max_id + 1, max_id + 1 + len(warning_data_df))
|
||||
# else:
|
||||
# warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
||||
# warning_data_df.to_sql(
|
||||
# table_name, con=engine, if_exists='append', index=False)
|
||||
# if is_update_warning_data:
|
||||
# upload_warning_info(len(warning_data_df))
|
||||
# except:
|
||||
# logger.info('上传预警信息到数据库失败')
|
||||
# 判断当前日期是不是周一
|
||||
is_weekday = datetime.datetime.now().weekday() == 0
|
||||
if is_weekday:
|
||||
logger.info('今天是周一，更新预测模型')
|
||||
# 计算最近60天预测残差最低的模型名称
|
||||
model_results = sqlitedb.select_data(
|
||||
'trueandpredict', order_by="ds DESC", limit="60")
|
||||
# 删除空值率为90%以上的列
|
||||
if len(model_results) > 10:
|
||||
model_results = model_results.dropna(
|
||||
thresh=len(model_results)*0.1, axis=1)
|
||||
# 删除空行
|
||||
model_results = model_results.dropna()
|
||||
modelnames = model_results.columns.to_list()[2:-1]
|
||||
for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||
model_results[col] = model_results[col].astype(np.float32)
|
||||
# 计算每个预测值与真实值之间的偏差率
|
||||
for model in modelnames:
|
||||
model_results[f'{model}_abs_error_rate'] = abs(
|
||||
model_results['y'] - model_results[model]) / model_results['y']
|
||||
# 获取每行对应的最小偏差率值
|
||||
min_abs_error_rate_values = model_results.apply(
|
||||
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
# 获取每行对应的最小偏差率值对应的列名
|
||||
min_abs_error_rate_column_name = model_results.apply(
|
||||
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
# 将列名索引转换为列名
|
||||
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
||||
lambda x: x.split('_')[0])
|
||||
# 取出现次数最多的模型名称
|
||||
most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# 保存结果到数据库
|
||||
if not sqlitedb.check_table_exists('most_model'):
|
||||
sqlitedb.create_table(
|
||||
'most_model', columns="ds datetime, most_common_model TEXT")
|
||||
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||
'%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
|
||||
# if is_corr:
|
||||
# df = corr_feature(df=df)
|
||||
try:
|
||||
if is_weekday:
|
||||
# if True:
|
||||
logger.info('今天是周一，发送特征预警')
|
||||
# 上传预警信息到数据库
|
||||
warning_data_df = df_zhibiaoliebiao.copy()
|
||||
warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
|
||||
'指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
|
||||
# 重命名列名
|
||||
warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
|
||||
'更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
||||
from sqlalchemy import create_engine
|
||||
import urllib
|
||||
global password
|
||||
if '@' in password:
|
||||
password = urllib.parse.quote_plus(password)
|
||||
|
||||
# df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
|
||||
# logger.info(f"开始训练模型...")
|
||||
# row, col = df.shape
|
||||
engine = create_engine(
|
||||
f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
|
||||
"%Y-%m-%d %H:%M:%S")
|
||||
warning_data_df['TENANT_CODE'] = 'T0004'
|
||||
# 插入数据之前查询表数据然后新增id列
|
||||
existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
||||
if not existing_data.empty:
|
||||
max_id = existing_data['ID'].astype(int).max()
|
||||
warning_data_df['ID'] = range(
|
||||
max_id + 1, max_id + 1 + len(warning_data_df))
|
||||
else:
|
||||
warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
||||
warning_data_df.to_sql(
|
||||
table_name, con=engine, if_exists='append', index=False)
|
||||
if is_update_warning_data:
|
||||
upload_warning_info(len(warning_data_df))
|
||||
except:
|
||||
logger.info('上传预警信息到数据库失败')
|
||||
|
||||
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||
# ex_Model(df,
|
||||
# horizon=global_config['horizon'],
|
||||
# input_size=global_config['input_size'],
|
||||
# train_steps=global_config['train_steps'],
|
||||
# val_check_steps=global_config['val_check_steps'],
|
||||
# early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
||||
# is_debug=global_config['is_debug'],
|
||||
# dataset=global_config['dataset'],
|
||||
# is_train=global_config['is_train'],
|
||||
# is_fivemodels=global_config['is_fivemodels'],
|
||||
# val_size=global_config['val_size'],
|
||||
# test_size=global_config['test_size'],
|
||||
# settings=global_config['settings'],
|
||||
# now=now,
|
||||
# etadata=global_config['etadata'],
|
||||
# modelsindex=global_config['modelsindex'],
|
||||
# data=data,
|
||||
# is_eta=global_config['is_eta'],
|
||||
# end_time=global_config['end_time'],
|
||||
# )
|
||||
if is_corr:
|
||||
df = corr_feature(df=df)
|
||||
|
||||
# logger.info('模型训练完成')
|
||||
df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
|
||||
logger.info(f"开始训练模型...")
|
||||
row, col = df.shape
|
||||
|
||||
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||
ex_Model(df,
|
||||
horizon=global_config['horizon'],
|
||||
input_size=global_config['input_size'],
|
||||
train_steps=global_config['train_steps'],
|
||||
val_check_steps=global_config['val_check_steps'],
|
||||
early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
||||
is_debug=global_config['is_debug'],
|
||||
dataset=global_config['dataset'],
|
||||
is_train=global_config['is_train'],
|
||||
is_fivemodels=global_config['is_fivemodels'],
|
||||
val_size=global_config['val_size'],
|
||||
test_size=global_config['test_size'],
|
||||
settings=global_config['settings'],
|
||||
now=now,
|
||||
etadata=global_config['etadata'],
|
||||
modelsindex=global_config['modelsindex'],
|
||||
data=data,
|
||||
is_eta=global_config['is_eta'],
|
||||
end_time=global_config['end_time'],
|
||||
)
|
||||
|
||||
logger.info('模型训练完成')
|
||||
|
||||
logger.info('训练数据绘图ing')
|
||||
model_results3 = model_losss(sqlitedb, end_time=end_time)
|
||||
@ -403,15 +421,15 @@ def predict_main():
|
||||
# 模型报告
|
||||
logger.info('制作报告ing')
|
||||
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
reportname = f'石油焦铝用大模型日度预测--{end_time}.pdf' # 报告文件名
|
||||
reportname = f'Brent原油大模型日度预测--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
shiyoujiao_lvyong_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||
brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||
reportname=reportname, sqlitedb=sqlitedb),
|
||||
|
||||
logger.info('制作报告end')
|
||||
logger.info('模型训练完成')
|
||||
|
||||
# push_market_value()
|
||||
push_market_value()
|
||||
|
||||
# # LSTM 单变量模型
|
||||
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
||||
|
||||
@ -301,7 +301,7 @@ def predict_main():
|
||||
thresh=len(model_results)*0.1, axis=1)
|
||||
# 删除空行
|
||||
model_results = model_results.dropna()
|
||||
modelnames = model_results.columns.to_list()[2:-2]
|
||||
modelnames = model_results.columns.to_list()[2:-1]
|
||||
for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||
model_results[col] = model_results[col].astype(np.float32)
|
||||
# 计算每个预测值与真实值之间的偏差率
|
||||
|
||||
@ -866,7 +866,7 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix):
|
||||
plt.text(i, j, str(j), ha='center', va='bottom')
|
||||
|
||||
# 当前日期画竖虚线
|
||||
plt.axvline(x=df['ds'].iloc[-config.horizon], color='r', linestyle='--')
|
||||
plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--')
|
||||
plt.legend()
|
||||
plt.xlabel('日期')
|
||||
plt.ylabel('价格')
|
||||
@ -881,8 +881,8 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix):
|
||||
ax.axis('off') # 关闭坐标轴
|
||||
# 数值保留2位小数
|
||||
df = df.round(2)
|
||||
df = df[-config.horizon:]
|
||||
df['Day'] = [f'Day_{i}' for i in range(1, config.horizon+1)]
|
||||
df = df[-horizon:]
|
||||
df['Day'] = [f'Day_{i}' for i in range(1, horizon+1)]
|
||||
# Day列放到最前面
|
||||
df = df[['Day'] + list(df.columns[:-1])]
|
||||
table = ax.table(cellText=df.values,
|
||||
@ -1297,7 +1297,7 @@ def model_losss(sqlitedb, end_time):
|
||||
# plt.plot(df['ds'], df[model], label=model,marker='o')
|
||||
plt.plot(df['ds'], df[most_model_name], label=model, marker='o')
|
||||
# 当前日期画竖虚线
|
||||
plt.axvline(x=df['ds'].iloc[-config.horizon], color='r', linestyle='--')
|
||||
plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--')
|
||||
plt.legend()
|
||||
plt.xlabel('日期')
|
||||
# 设置横轴日期格式为年-月-日
|
||||
@ -1338,7 +1338,7 @@ def model_losss(sqlitedb, end_time):
|
||||
plt.text(i, j, str(j), ha='center', va='bottom')
|
||||
|
||||
# 当前日期画竖虚线
|
||||
plt.axvline(x=df['ds'].iloc[-config.horizon], color='r', linestyle='--')
|
||||
plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--')
|
||||
plt.legend()
|
||||
plt.xlabel('日期')
|
||||
# 自动设置横轴日期显示
|
||||
@ -1357,8 +1357,8 @@ def model_losss(sqlitedb, end_time):
|
||||
ax.axis('off') # 关闭坐标轴
|
||||
# 数值保留2位小数
|
||||
df = df.round(2)
|
||||
df = df[-config.horizon:]
|
||||
df['Day'] = [f'Day_{i}' for i in range(1, config.horizon+1)]
|
||||
df = df[-horizon:]
|
||||
df['Day'] = [f'Day_{i}' for i in range(1, horizon+1)]
|
||||
# Day列放到最前面
|
||||
df = df[['Day'] + list(df.columns[:-1])]
|
||||
table = ax.table(cellText=df.values,
|
||||
@ -1388,10 +1388,10 @@ def model_losss(sqlitedb, end_time):
|
||||
bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
_plt_predict_ture(df_combined3)
|
||||
# _plt_predict_ture(df_combined3)
|
||||
# _plt_modeltopten_predict_ture(df_combined4)
|
||||
_plt_predict_table(df_combined3)
|
||||
_plt_model_results3()
|
||||
# _plt_predict_table(df_combined3)
|
||||
# _plt_model_results3()
|
||||
|
||||
return model_results3
|
||||
|
||||
@ -2461,319 +2461,6 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
|
||||
print(f"请求超时: {e}")
|
||||
|
||||
|
||||
@exception_logger
|
||||
def shiyoujiao_lvyong_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, inputsize=5, dataset='dataset', time='2024-07-30', reportname='report.pdf', sqlitedb='jbsh_yuanyou.db'):
|
||||
global y
|
||||
# 创建内容对应的空列表
|
||||
content = list()
|
||||
# 获取特征的近一月值
|
||||
import pandas as pd
|
||||
feature_data_df = pd.read_csv(os.path.join(
|
||||
config.dataset,'指标数据添加时间特征.csv'), parse_dates=['ds']).tail(60)
|
||||
|
||||
def draw_feature_trend(feature_data_df, features):
|
||||
# 画特征近60天的趋势图
|
||||
feature_df = feature_data_df[['ds', 'y']+features]
|
||||
# 遍历X每一列，和yy画散点图 ，
|
||||
|
||||
for i, col in enumerate(features):
|
||||
# try:
|
||||
print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
|
||||
if col not in ['ds', 'y']:
|
||||
fig, ax1 = plt.subplots(figsize=(10, 6))
|
||||
# 在第一个坐标轴上绘制数据
|
||||
sns.lineplot(data=feature_df, x='ds', y='y', ax=ax1, color='b')
|
||||
ax1.set_xlabel('日期')
|
||||
ax1.set_ylabel('y', color='b')
|
||||
ax1.tick_params('y', colors='b')
|
||||
# 在 ax1 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||||
for j in range(1, len(feature_df), 2):
|
||||
value = feature_df['y'].iloc[j]
|
||||
date = feature_df['ds'].iloc[j]
|
||||
offset = 1.001
|
||||
ax1.text(date, value * offset, str(round(value, 2)),
|
||||
ha='center', va='bottom', color='b', fontsize=10)
|
||||
# 创建第二个坐标轴
|
||||
ax2 = ax1.twinx()
|
||||
# 在第二个坐标轴上绘制数据
|
||||
sns.lineplot(data=feature_df, x='ds', y=col, ax=ax2, color='r')
|
||||
ax2.set_ylabel(col, color='r')
|
||||
ax2.tick_params('y', colors='r')
|
||||
# 在 ax2 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||||
for j in range(0, len(feature_df), 2):
|
||||
value = feature_df[col].iloc[j]
|
||||
date = feature_df['ds'].iloc[j]
|
||||
offset = 1.0003
|
||||
ax2.text(date, value * offset, str(round(value, 2)),
|
||||
ha='center', va='bottom', color='r', fontsize=10)
|
||||
# 添加标题
|
||||
plt.title(col)
|
||||
# 设置横坐标为日期格式并自动调整
|
||||
locator = mdates.AutoDateLocator()
|
||||
formatter = mdates.AutoDateFormatter(locator)
|
||||
ax1.xaxis.set_major_locator(locator)
|
||||
ax1.xaxis.set_major_formatter(formatter)
|
||||
# 文件名特殊字符处理
|
||||
col = col.replace('*', '-')
|
||||
col = col.replace(':', '-')
|
||||
col = col.replace(r'/', '-')
|
||||
plt.savefig(os.path.join(config.dataset, f'{col}与价格散点图.png'))
|
||||
content.append(Graphs.draw_img(
|
||||
os.path.join(config.dataset, f'{col}与价格散点图.png')))
|
||||
plt.close()
|
||||
# except Exception as e:
|
||||
# print(f'绘制第{i+1}个特征{col}与价格散点图时出错：{e}')
|
||||
|
||||
# 添加标题
|
||||
content.append(Graphs.draw_title(f'{config.y}{time}预测报告'))
|
||||
|
||||
# 预测结果
|
||||
content.append(Graphs.draw_little_title('一、预测结果：'))
|
||||
# 添加历史走势及预测价格的走势图片
|
||||
content.append(Graphs.draw_img(os.path.join(config.dataset, '历史价格-预测值.png')))
|
||||
# 波动率画图逻辑
|
||||
content.append(Graphs.draw_text('图示说明：'))
|
||||
content.append(Graphs.draw_text(
|
||||
' 确定置信区间：设置残差置信阈值，以每周最佳模型为基准，选取在置信区间的预测值作为置信区间;'))
|
||||
|
||||
|
||||
# 取df中y列为空的行
|
||||
import pandas as pd
|
||||
df = pd.read_csv(os.path.join(config.dataset, 'predict.csv'), encoding='gbk')
|
||||
df_true = pd.read_csv(os.path.join(
|
||||
config.dataset,'指标数据添加时间特征.csv'), encoding='utf-8') # 获取预测日期对应的真实值
|
||||
df_true = df_true[['ds', 'y']]
|
||||
eval_df = pd.read_csv(os.path.join(
|
||||
config.dataset,'model_evaluation.csv'), encoding='utf-8')
|
||||
# 按评估指标排序，取前五
|
||||
fivemodels_list = eval_df['模型(Model)'].values # 列表形式，后面当作列名索引使用
|
||||
# 取 fivemodels_list 和 ds 列
|
||||
df = df[['ds'] + fivemodels_list.tolist()]
|
||||
# 拼接预测日期对应的真实值
|
||||
df = pd.merge(df, df_true, on='ds', how='left')
|
||||
# 删除全部为nan的列
|
||||
df = df.dropna(how='all', axis=1)
|
||||
# 选择除 'ds' 列外的数值列，并进行类型转换和四舍五入
|
||||
num_cols = [col for col in df.columns if col !=
|
||||
'ds' and pd.api.types.is_numeric_dtype(df[col])]
|
||||
for col in num_cols:
|
||||
df[col] = df[col].astype(float).round(2)
|
||||
# 添加最大值、最小值、平均值三列
|
||||
df['平均值'] = df[num_cols].mean(axis=1).round(2)
|
||||
df['最大值'] = df[num_cols].max(axis=1)
|
||||
df['最小值'] = df[num_cols].min(axis=1)
|
||||
# df转置
|
||||
df = df.T
|
||||
# df重置索引
|
||||
df = df.reset_index()
|
||||
# 添加预测值表格
|
||||
data = df.values.tolist()
|
||||
col_width = 500/len(df.columns)
|
||||
content.append(Graphs.draw_table(col_width, *data))
|
||||
content.append(Graphs.draw_little_title('二、上一预测周期偏差率分析：'))
|
||||
df = pd.read_csv(os.path.join(
|
||||
config.dataset,'testandpredict_groupby.csv'), encoding='utf-8')
|
||||
df4 = df.copy() # 计算偏差率使用
|
||||
# 去掉created_dt 列
|
||||
df4 = df4.drop(columns=['created_dt'])
|
||||
# 计算模型偏差率
|
||||
# 计算各列对于y列的差值百分比
|
||||
df3 = pd.DataFrame() # 存储偏差率
|
||||
|
||||
# 删除有null的行
|
||||
df4 = df4.dropna()
|
||||
df3['ds'] = df4['ds']
|
||||
for col in fivemodels_list:
|
||||
df3[col] = round(abs(df4[col] - df4['y']) / df4['y'] * 100, 2)
|
||||
# 找出决定系数前五的偏差率
|
||||
df3 = df3[['ds']+fivemodels_list.tolist()][-inputsize:]
|
||||
# 找出上一预测区间的时间
|
||||
stime = df3['ds'].iloc[0]
|
||||
etime = df3['ds'].iloc[-1]
|
||||
# 添加偏差率表格
|
||||
fivemodels = '、'.join(eval_df['模型(Model)'].values[:5]) # 字符串形式，后面写入字符串使用
|
||||
content.append(Graphs.draw_text(
|
||||
f'预测使用了{num_models}个模型进行训练，使用评估结果MAE前五的模型分别是 {fivemodels} ，模型上一预测区间 {stime} -- {etime}的偏差率(%)分别是：'))
|
||||
# # 添加偏差率表格
|
||||
df3 = df3.T
|
||||
df3 = df3.reset_index()
|
||||
data = df3.values.tolist()
|
||||
col_width = 500/len(df3.columns)
|
||||
content.append(Graphs.draw_table(col_width, *data))
|
||||
|
||||
content.append(Graphs.draw_little_title('上一周预测准确率：'))
|
||||
df4 = sqlitedb.select_data('accuracy_rote', order_by='结束日期 desc', limit=1)
|
||||
df4 = df4.T
|
||||
df4 = df4.reset_index()
|
||||
df4 = df4.T
|
||||
data = df4.values.tolist()
|
||||
col_width = 500/len(df4.columns)
|
||||
content.append(Graphs.draw_table(col_width, *data))
|
||||
|
||||
content.append(Graphs.draw_little_title('三、预测过程解析：'))
|
||||
# 特征、模型、参数配置
|
||||
content.append(Graphs.draw_little_title('模型选择：'))
|
||||
content.append(Graphs.draw_text(
|
||||
f'本次预测使用了一个专门收集时间序列的NeuralForecast库中的{num_models}个模型：'))
|
||||
content.append(Graphs.draw_text(f'使用40天的数据预测未来{inputsize}天的数据。'))
|
||||
content.append(Graphs.draw_little_title('指标情况：'))
|
||||
with open(os.path.join(config.dataset, '特征频度统计.txt'), encoding='utf-8') as f:
|
||||
for line in f.readlines():
|
||||
content.append(Graphs.draw_text(line))
|
||||
|
||||
data = pd.read_csv(os.path.join(config.dataset, '指标数据添加时间特征.csv'),
|
||||
encoding='utf-8') # 计算相关系数用
|
||||
df_zhibiaofenlei = loadcsv(os.path.join(
|
||||
config.dataset,'特征处理后的指标名称及分类.csv')) # 气泡图用
|
||||
df_zhibiaoshuju = data.copy() # 气泡图用
|
||||
|
||||
# 绘制特征相关气泡图
|
||||
|
||||
grouped = df_zhibiaofenlei.groupby('指标分类')
|
||||
grouped_corr = pd.DataFrame(columns=['指标分类', '指标数量', '相关性总和'])
|
||||
|
||||
content.append(Graphs.draw_little_title('按指标分类分别与预测目标进行皮尔逊相关系数分析：'))
|
||||
content.append(Graphs.draw_text('''皮尔逊相关系数说明：'''))
|
||||
content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。'''))
|
||||
content.append(Graphs.draw_text('''
|
||||
相关系数为1：表示两个变量之间存在完全正向的线性关系，即当一个变量增加时，另一个变量也相应增加，且变化是完全一致的。'''))
|
||||
content.append(Graphs.draw_text(
|
||||
'''相关系数为-1：表示两个变量之间存在完全负向的线性关系，即当一个变量增加时，另一个变量会相应减少，且变化是完全相反的'''))
|
||||
content.append(Graphs.draw_text(
|
||||
'''相关系数接近0：表示两个变量之间不存在线性关系，即它们的变化不会随着对方的变化而变化。'''))
|
||||
for name, group in grouped:
|
||||
cols = group['指标名称'].tolist()
|
||||
config.logger.info(f'开始绘制{name}类指标的相关性直方图')
|
||||
cols_subset = cols
|
||||
feature_names = ['y'] + cols_subset
|
||||
correlation_matrix = df_zhibiaoshuju[feature_names].corr()['y']
|
||||
|
||||
# 绘制特征相关性直方分布图
|
||||
plt.figure(figsize=(10, 8))
|
||||
sns.histplot(correlation_matrix.values.flatten(),
|
||||
bins=20, kde=True, color='skyblue')
|
||||
plt.title(f'{name}类指标(共{len(cols_subset)}个)相关性直方分布图')
|
||||
plt.xlabel('相关系数')
|
||||
plt.ylabel('频数')
|
||||
plt.savefig(os.path.join(
|
||||
config.dataset,f'{name}类指标相关性直方分布图.png'), bbox_inches='tight')
|
||||
plt.close()
|
||||
content.append(Graphs.draw_img(
|
||||
os.path.join(config.dataset, f'{name}类指标相关性直方分布图.png')))
|
||||
content.append(Graphs.draw_text(
|
||||
f'{name}类指标(共{len(cols_subset)}个)的相关性直方分布图如上所示。'))
|
||||
# 相关性大于0的特征
|
||||
positive_corr_features = correlation_matrix[correlation_matrix > 0].sort_values(
|
||||
ascending=False).index.tolist()[1:]
|
||||
|
||||
print(f'{name}下正相关的特征值有：', positive_corr_features)
|
||||
if len(positive_corr_features) > 5:
|
||||
positive_corr_features = positive_corr_features[0:5]
|
||||
content.append(Graphs.draw_text(
|
||||
f'{name}类指标中，与预测目标y正相关前五的特征有：{positive_corr_features}'))
|
||||
draw_feature_trend(feature_data_df, positive_corr_features)
|
||||
elif len(positive_corr_features) == 0:
|
||||
pass
|
||||
else:
|
||||
positive_corr_features = positive_corr_features
|
||||
content.append(Graphs.draw_text(
|
||||
f'其中，与预测目标y正相关的特征有：{positive_corr_features}'))
|
||||
draw_feature_trend(feature_data_df, positive_corr_features)
|
||||
|
||||
# 相关性小于0的特征
|
||||
negative_corr_features = correlation_matrix[correlation_matrix < 0].sort_values(
|
||||
ascending=True).index.tolist()
|
||||
|
||||
print(f'{name}下负相关的特征值有：', negative_corr_features)
|
||||
if len(negative_corr_features) > 5:
|
||||
negative_corr_features = negative_corr_features[:5]
|
||||
content.append(Graphs.draw_text(
|
||||
f'与预测目标y负相关前五的特征有：{negative_corr_features}'))
|
||||
draw_feature_trend(feature_data_df, negative_corr_features)
|
||||
elif len(negative_corr_features) == 0:
|
||||
pass
|
||||
else:
|
||||
content.append(Graphs.draw_text(
|
||||
f'{name}类指标中，与预测目标y负相关的特征有：{negative_corr_features}'))
|
||||
draw_feature_trend(feature_data_df, negative_corr_features)
|
||||
# 计算correlation_sum 第一行的相关性的绝对值的总和
|
||||
correlation_sum = correlation_matrix.abs().sum()
|
||||
config.logger.info(f'{name}类指标的相关性总和为：{correlation_sum}')
|
||||
# 分组的相关性总和拼接到grouped_corr
|
||||
goup_corr = pd.DataFrame(
|
||||
{'指标分类': [name], '指标数量': [len(cols_subset)], '相关性总和': [correlation_sum]})
|
||||
grouped_corr = pd.concat(
|
||||
[grouped_corr, goup_corr], axis=0, ignore_index=True)
|
||||
|
||||
# 绘制相关性总和的气泡图
|
||||
config.logger.info(f'开始绘制相关性总和的气泡图')
|
||||
plt.figure(figsize=(10, 10))
|
||||
sns.scatterplot(data=grouped_corr, x='相关性总和', y='指标数量', size='相关性总和', sizes=(
|
||||
grouped_corr['相关性总和'].min()*5, grouped_corr['相关性总和'].max()*5), hue='指标分类', palette='viridis')
|
||||
plt.title('指标分类相关性总和的气泡图')
|
||||
plt.ylabel('数量')
|
||||
plt.savefig(os.path.join(config.dataset, '指标分类相关性总和的气泡图.png'),
|
||||
bbox_inches='tight')
|
||||
plt.close()
|
||||
content.append(Graphs.draw_img(os.path.join(config.dataset, '指标分类相关性总和的气泡图.png')))
|
||||
content.append(Graphs.draw_text(
|
||||
'气泡图中，横轴为指标分类，纵轴为指标分类下的特征数量，气泡的面积越大表示该分类中特征的相关系数和越大。'))
|
||||
config.logger.info(f'绘制相关性总和的气泡图结束')
|
||||
content.append(Graphs.draw_little_title('模型选择：'))
|
||||
content.append(Graphs.draw_text(
|
||||
f'预测使用了{num_models}个模型进行训练拟合，通过评估指标MAE从小到大排列，前5个模型的简介如下：'))
|
||||
# 读取模型简介
|
||||
with open(os.path.join(config.dataset, 'model_introduction.txt'), 'r', encoding='utf-8') as f:
|
||||
for line in f:
|
||||
line_split = line.strip().split('--')
|
||||
if line_split[0] in fivemodels_list:
|
||||
for introduction in line_split:
|
||||
content.append(Graphs.draw_text(introduction))
|
||||
content.append(Graphs.draw_little_title('模型评估：'))
|
||||
df = pd.read_csv(os.path.join(
|
||||
config.dataset,'model_evaluation.csv'), encoding='utf-8')
|
||||
# 判断 df 的数值列转为float
|
||||
for col in eval_df.columns:
|
||||
if col not in ['模型(Model)']:
|
||||
eval_df[col] = eval_df[col].astype(float)
|
||||
eval_df[col] = eval_df[col].round(3)
|
||||
# 筛选 fivemodels_list.tolist() 的行
|
||||
eval_df = eval_df[eval_df['模型(Model)'].isin(fivemodels_list)]
|
||||
# df转置
|
||||
eval_df = eval_df.T
|
||||
# df重置索引
|
||||
eval_df = eval_df.reset_index()
|
||||
eval_df = eval_df.T
|
||||
# # 添加表格
|
||||
data = eval_df.values.tolist()
|
||||
col_width = 500/len(eval_df.columns)
|
||||
content.append(Graphs.draw_table(col_width, *data))
|
||||
content.append(Graphs.draw_text('评估指标释义：'))
|
||||
content.append(Graphs.draw_text(
|
||||
'1. 均方根误差(RMSE)：均方根误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||||
content.append(Graphs.draw_text(
|
||||
'2. 平均绝对误差(MAE)：平均绝对误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||||
content.append(Graphs.draw_text(
|
||||
'3. 平均平方误差(MSE)：平均平方误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||||
content.append(Graphs.draw_text('模型拟合：'))
|
||||
# 添加图片
|
||||
content.append(Graphs.draw_img(os.path.join(config.dataset, '预测值与真实值对比图.png')))
|
||||
# 生成pdf文件
|
||||
doc = SimpleDocTemplate(os.path.join(config.dataset, reportname), pagesize=letter)
|
||||
doc.build(content)
|
||||
# pdf 上传到数字化信息平台
|
||||
try:
|
||||
if config.is_update_report:
|
||||
with open(os.path.join(config.dataset, reportname), 'rb') as f:
|
||||
base64_data = base64.b64encode(f.read()).decode('utf-8')
|
||||
upload_data["data"]["fileBase64"] = base64_data
|
||||
upload_data["data"]["fileName"] = reportname
|
||||
token = get_head_auth_report()
|
||||
upload_report_data(token, upload_data)
|
||||
except TimeoutError as e:
|
||||
print(f"请求超时: {e}")
|
||||
|
||||
|
||||
@exception_logger
|
||||
def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, inputsize=5, dataset='dataset', time='2024-07-30', reportname='report.pdf', sqlitedb='jbsh_yuanyou.db'):
|
||||
# 创建内容对应的空列表
|
||||
|
||||
489
test/ETA获取数据.py
489
test/ETA获取数据.py
@ -11,9 +11,11 @@ import logging.handlers
|
||||
import os
|
||||
import re
|
||||
import requests
|
||||
import json
|
||||
|
||||
|
||||
class EtaReader():
|
||||
def __init__(self,signature,classifylisturl,classifyidlisturl,edbcodedataurl,edbcodelist,edbdatapushurl,edbdeleteurl,edbbusinessurl):
|
||||
def __init__(self, signature, classifylisturl, classifyidlisturl, edbcodedataurl, edbcodelist, edbdatapushurl, edbdeleteurl, edbbusinessurl):
|
||||
'''
|
||||
初始化 EtaReader 类的实例。
|
||||
|
||||
@ -39,17 +41,16 @@ class EtaReader():
|
||||
self.edbdeleteurl = edbdeleteurl
|
||||
self.edbbusinessurl = edbbusinessurl
|
||||
|
||||
|
||||
def filter_yuanyou_data(self,ClassifyName,data):
|
||||
def filter_yuanyou_data(self, ClassifyName, data):
|
||||
'''
|
||||
指标名称保留规则
|
||||
'''
|
||||
|
||||
# 包含 关键词 去除， 返回flase
|
||||
if any(keyword in data for keyword in ['运费','检修','波动率','地缘政治','股价',
|
||||
'同比','环比','环差','裂差','4WMA','变频','道琼斯','标普500','纳斯达克',
|
||||
'四周均值','名占比','残差','DMA',
|
||||
'连7-连9','4周平均','4周均值','滚动相关性','日本']):
|
||||
if any(keyword in data for keyword in ['运费', '检修', '波动率', '地缘政治', '股价',
|
||||
'同比', '环比', '环差', '裂差', '4WMA', '变频', '道琼斯', '标普500', '纳斯达克',
|
||||
'四周均值', '名占比', '残差', 'DMA',
|
||||
'连7-连9', '4周平均', '4周均值', '滚动相关性', '日本']):
|
||||
return False
|
||||
|
||||
# 检查需要的特征
|
||||
@ -59,7 +60,7 @@ class EtaReader():
|
||||
|
||||
# 保留 库存中特殊关键词
|
||||
if ClassifyName == '库存':
|
||||
if any(keyword in data for keyword in ['原油' , '美国' ,'全球' ,'中国' ,'富查伊拉','ARA' ]):
|
||||
if any(keyword in data for keyword in ['原油', '美国', '全球', '中国', '富查伊拉', 'ARA']):
|
||||
return True
|
||||
else:
|
||||
pass
|
||||
@ -77,8 +78,8 @@ class EtaReader():
|
||||
|
||||
# 去掉 航班中不是中国、美国 的数据
|
||||
if ClassifyName == '需求':
|
||||
if '航班' in data :
|
||||
if '中国' in data or '美国' in data :
|
||||
if '航班' in data:
|
||||
if '中国' in data or '美国' in data:
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
@ -95,7 +96,7 @@ class EtaReader():
|
||||
c = int(data.split('c1-c')[1])
|
||||
except:
|
||||
return False
|
||||
if c > 9 :
|
||||
if c > 9:
|
||||
return False
|
||||
else:
|
||||
pass
|
||||
@ -104,24 +105,24 @@ class EtaReader():
|
||||
pass
|
||||
|
||||
# 判断 同质性数据， 字符串开头
|
||||
strstartdict = {'ICE Brent c':"ICE Brent c14",
|
||||
'NYMWX WTI c':"NYMWX WTI c5",
|
||||
'INE SC c':"INE SC c1",
|
||||
'EFS c':"EFS c",
|
||||
'Dubai Swap c':"Dubai Swap c1",
|
||||
'Oman Swap c':"Oman Swap c1",
|
||||
'DME Oman c':"DME Oman c1",
|
||||
'Murban Futures c':"Murban Futures c1",
|
||||
'Dubai连合约价格':'Dubai连1合约价格',
|
||||
'美国RBOB期货月份合约价格':'美国RBOB期货2309月份合约价格',
|
||||
'Brent连合约价格':'Brent连1合约价格',
|
||||
'WTI连合约价格':'WTI连1合约价格',
|
||||
'布伦特连合约价格':'Brent连1合约价格',
|
||||
'Brent 连合约价格':'Brent连1合约价格',
|
||||
'Dubai连合约价格':'Dubai连1合约价格',
|
||||
'Brent连':'Brent连1合约价格',
|
||||
'brent连':'Brent连1合约价格',
|
||||
}
|
||||
strstartdict = {'ICE Brent c': "ICE Brent c14",
|
||||
'NYMWX WTI c': "NYMWX WTI c5",
|
||||
'INE SC c': "INE SC c1",
|
||||
'EFS c': "EFS c",
|
||||
'Dubai Swap c': "Dubai Swap c1",
|
||||
'Oman Swap c': "Oman Swap c1",
|
||||
'DME Oman c': "DME Oman c1",
|
||||
'Murban Futures c': "Murban Futures c1",
|
||||
'Dubai连合约价格': 'Dubai连1合约价格',
|
||||
'美国RBOB期货月份合约价格': '美国RBOB期货2309月份合约价格',
|
||||
'Brent连合约价格': 'Brent连1合约价格',
|
||||
'WTI连合约价格': 'WTI连1合约价格',
|
||||
'布伦特连合约价格': 'Brent连1合约价格',
|
||||
'Brent 连合约价格': 'Brent连1合约价格',
|
||||
'Dubai连合约价格': 'Dubai连1合约价格',
|
||||
'Brent连': 'Brent连1合约价格',
|
||||
'brent连': 'Brent连1合约价格',
|
||||
}
|
||||
# 判断名称字符串开头是否在 strstartdict.keys中
|
||||
match = re.match(r'([a-zA-Z\s]+)(\d+)', data)
|
||||
if match:
|
||||
@ -135,11 +136,11 @@ class EtaReader():
|
||||
# data = 'Brent 连7合约价格'
|
||||
# 判断名称字符串去掉数字后是否在 strstartdict.keys中
|
||||
match = re.findall(r'\D+', data)
|
||||
if match :
|
||||
if match:
|
||||
if len(match) == 2:
|
||||
part1 = match[0]
|
||||
part2 = match[1]
|
||||
if part1+part2 in [i for i in strstartdict.keys()]:
|
||||
if part1+part2 in [i for i in strstartdict.keys()]:
|
||||
if data == strstartdict[part1+part2]:
|
||||
return True
|
||||
else:
|
||||
@ -150,7 +151,7 @@ class EtaReader():
|
||||
match = re.findall(r'\D+', data)
|
||||
part1 = match[0]
|
||||
|
||||
if part1 in [i for i in strstartdict.keys()]:
|
||||
if part1 in [i for i in strstartdict.keys()]:
|
||||
if data == strstartdict[part1]:
|
||||
return True
|
||||
else:
|
||||
@ -166,7 +167,7 @@ class EtaReader():
|
||||
|
||||
return True
|
||||
|
||||
def filter_pp_data(self,ClassifyName,data):
|
||||
def filter_pp_data(self, ClassifyName, data):
|
||||
'''
|
||||
指标名称保留规则
|
||||
'''
|
||||
@ -181,8 +182,6 @@ class EtaReader():
|
||||
if any(keyword in data for keyword in ['拉丝']):
|
||||
return True
|
||||
|
||||
|
||||
|
||||
# 检查需要的特征
|
||||
# 去掉 期货市场 分类下的数据
|
||||
if ClassifyName == '期货市场':
|
||||
@ -214,18 +213,16 @@ class EtaReader():
|
||||
else:
|
||||
pass
|
||||
|
||||
|
||||
# 保留 需求 下所有指标
|
||||
if ClassifyName == '需求':
|
||||
return True
|
||||
else:
|
||||
pass
|
||||
|
||||
|
||||
return True
|
||||
|
||||
# 通过edbcode 获取指标数据
|
||||
def edbcodegetdata(self,df,EdbCode,EdbName):
|
||||
def edbcodegetdata(self, df, EdbCode, EdbName):
|
||||
# 根据指标id，获取指标数据
|
||||
url = self.edbcodedataurl+str(EdbCode)
|
||||
# 发送GET请求
|
||||
@ -236,7 +233,8 @@ class EtaReader():
|
||||
data = response.json() # 假设接口返回的是JSON数据
|
||||
all_data_items = data.get('Data')
|
||||
# 列表转换为DataFrame
|
||||
df3 = pd.DataFrame(all_data_items, columns=['DataTime', 'Value', 'UpdateTime'])
|
||||
df3 = pd.DataFrame(all_data_items, columns=[
|
||||
'DataTime', 'Value', 'UpdateTime'])
|
||||
# df3 = pd.read_json(all_data_items, orient='records')
|
||||
|
||||
# 去掉UpdateTime 列
|
||||
@ -244,7 +242,8 @@ class EtaReader():
|
||||
# df3.set_index('DataTime')
|
||||
df3.rename(columns={'Value': EdbName}, inplace=True)
|
||||
# 将数据存储df1
|
||||
df = pd.merge(df, df3, how='outer',on='DataTime',suffixes= ('', '_y'))
|
||||
df = pd.merge(df, df3, how='outer',
|
||||
on='DataTime', suffixes=('', '_y'))
|
||||
# 按时间排序
|
||||
df = df.sort_values(by='DataTime', ascending=True)
|
||||
return df
|
||||
@ -255,7 +254,7 @@ class EtaReader():
|
||||
# 主动抛出异常
|
||||
raise Exception(f'Error: {response.status_code}, {response.text}')
|
||||
|
||||
def get_eta_api_yuanyou_data(self,data_set,dataset=''):
|
||||
def get_eta_api_yuanyou_data(self, data_set, dataset=''):
|
||||
'''
|
||||
从ETA API获取原油数据
|
||||
|
||||
@ -271,7 +270,8 @@ class EtaReader():
|
||||
# 定义你的headers，这里可以包含多个参数
|
||||
self.headers = {
|
||||
'nonce': self.signature.nonce, # 例如，一个认证令牌
|
||||
'timestamp': str(self.signature.timestamp), # 自定义的header参数
|
||||
# 自定义的header参数
|
||||
'timestamp': str(self.signature.timestamp),
|
||||
'appid': self.signature.APPID, # 另一个自定义的header参数
|
||||
'signature': self.signature.signature
|
||||
}
|
||||
@ -285,16 +285,16 @@ class EtaReader():
|
||||
'''
|
||||
|
||||
# 构建新的DataFrame df df1
|
||||
df = pd.DataFrame(columns=['指标分类', '指标名称', '指标id', '频度','指标来源','来源id','最后更新时间','更新周期','预警日期','停更周期'])
|
||||
df = pd.DataFrame(columns=[
|
||||
'指标分类', '指标名称', '指标id', '频度', '指标来源', '来源id', '最后更新时间', '更新周期', '预警日期', '停更周期'])
|
||||
df1 = pd.DataFrame(columns=['DataTime'])
|
||||
|
||||
|
||||
# 外网环境无法访问，请确认是否为内网环境
|
||||
try:
|
||||
# 发送GET请求 获取指标分类列表
|
||||
response = requests.get(self.classifylisturl, headers=self.headers)
|
||||
except requests.exceptions.RequestException as e:
|
||||
raise Exception(f"请求失败,请确认是否为内网环境: {e}","\033[0m")
|
||||
raise Exception(f"请求失败,请确认是否为内网环境: {e}", "\033[0m")
|
||||
|
||||
# 检查响应状态码
|
||||
if response.status_code == 200:
|
||||
@ -304,19 +304,20 @@ class EtaReader():
|
||||
# 请求成功，处理响应内容
|
||||
# logger.info(data.get('Data'))
|
||||
# 定义你想要保留的固定值
|
||||
fixed_value = 1214
|
||||
fixed_value = 1193
|
||||
|
||||
# 遍历列表，只保留那些'category' key的值为固定值的数据项
|
||||
filtered_data = [item for item in data.get('Data') if item.get('ParentId') == fixed_value]
|
||||
filtered_data = [item for item in data.get(
|
||||
'Data') if item.get('ParentId') == fixed_value]
|
||||
|
||||
#然后循环filtered_data去获取list数据，才能获取到想要获取的ClassifyId
|
||||
# 然后循环filtered_data去获取list数据，才能获取到想要获取的ClassifyId
|
||||
n = 0
|
||||
for item in filtered_data:
|
||||
n+= 1
|
||||
n += 1
|
||||
# if n>50:
|
||||
# break
|
||||
ClassifyId = item["ClassifyId"] #分类id，分类下的指标列表接口的请求参数
|
||||
ClassifyName = item["ClassifyName"] #分类名称，要保存到df的指标分类列
|
||||
ClassifyId = item["ClassifyId"] # 分类id，分类下的指标列表接口的请求参数
|
||||
ClassifyName = item["ClassifyName"] # 分类名称，要保存到df的指标分类列
|
||||
# 根据分类id，获取指标列表
|
||||
url = self.classifyidlisturl+str(ClassifyId)
|
||||
response = requests.get(url, headers=self.headers)
|
||||
@ -327,12 +328,13 @@ class EtaReader():
|
||||
for i in Data:
|
||||
# s+= 1
|
||||
EdbCode = i.get('EdbCode')
|
||||
EdbName = i.get('EdbName') # 指标名称，要保存到df2的指标名称列,df的指标名称列
|
||||
# 指标名称，要保存到df2的指标名称列,df的指标名称列
|
||||
EdbName = i.get('EdbName')
|
||||
Frequency = i.get('Frequency') # 频度，要保存到df的频度列
|
||||
SourceName = i.get('SourceName') # 来源名称，要保存到df的频度列
|
||||
Source = i.get('Source') # 来源ID，要保存到df的频度列
|
||||
# 频度不是 日 或者 周的 跳过
|
||||
if Frequency not in ['日度','周度','日','周']:
|
||||
if Frequency not in ['日度', '周度', '日', '周']:
|
||||
continue
|
||||
|
||||
# 只保留手工数据中，名称带有 海运出口 海运进口
|
||||
@ -343,44 +345,53 @@ class EtaReader():
|
||||
if Source == 2:
|
||||
continue
|
||||
|
||||
|
||||
# 判断名称是否需要保存
|
||||
isSave = self.filter_yuanyou_data(ClassifyName,EdbName)
|
||||
isSave = self.filter_yuanyou_data(
|
||||
ClassifyName, EdbName)
|
||||
if isSave:
|
||||
# 保存到df
|
||||
df1 = self.edbcodegetdata(df1,EdbCode,EdbName)
|
||||
df1 = self.edbcodegetdata(df1, EdbCode, EdbName)
|
||||
# 取df1所有行最后一列
|
||||
edbname_df = df1[['DataTime',f'{EdbName}']]
|
||||
edbname_df = df1[['DataTime', f'{EdbName}']]
|
||||
edbname_df = edbname_df.dropna()
|
||||
|
||||
if len(edbname_df) == 0:
|
||||
logger.info(f'指标名称：{EdbName} 没有数据')
|
||||
continue
|
||||
try:
|
||||
time_sequence = edbname_df['DataTime'].values.tolist()[-10:]
|
||||
time_sequence = edbname_df['DataTime'].values.tolist(
|
||||
)[-10:]
|
||||
except IndexError:
|
||||
time_sequence = edbname_df['DataTime'].values.tolist()
|
||||
time_sequence = edbname_df['DataTime'].values.tolist(
|
||||
)
|
||||
# 使用Counter来统计每个星期几的出现次数
|
||||
from collections import Counter
|
||||
weekday_counter = Counter(datetime.datetime.strptime(time_str, "%Y-%m-%d").strftime('%A') for time_str in time_sequence)
|
||||
weekday_counter = Counter(datetime.datetime.strptime(
|
||||
time_str, "%Y-%m-%d").strftime('%A') for time_str in time_sequence)
|
||||
|
||||
# 打印出现次数最多的星期几
|
||||
try:
|
||||
most_common_weekday = weekday_counter.most_common(1)[0][0]
|
||||
most_common_weekday = weekday_counter.most_common(1)[
|
||||
0][0]
|
||||
# 计算两周后的日期
|
||||
warning_date = (datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d") + datetime.timedelta(weeks=2)).strftime("%Y-%m-%d")
|
||||
stop_update_period = (datetime.datetime.strptime(today, "%Y-%m-%d") - datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d")).days // 7
|
||||
warning_date = (datetime.datetime.strptime(
|
||||
time_sequence[-1], "%Y-%m-%d") + datetime.timedelta(weeks=2)).strftime("%Y-%m-%d")
|
||||
stop_update_period = (datetime.datetime.strptime(
|
||||
today, "%Y-%m-%d") - datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d")).days // 7
|
||||
|
||||
except IndexError:
|
||||
most_common_weekday = '其他'
|
||||
stop_update_period = 0
|
||||
if '日' in Frequency:
|
||||
most_common_weekday = '每天'
|
||||
warning_date = (datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d") + datetime.timedelta(days=3)).strftime("%Y-%m-%d")
|
||||
stop_update_period = (datetime.datetime.strptime(today, "%Y-%m-%d") - datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d")).days
|
||||
warning_date = (datetime.datetime.strptime(
|
||||
time_sequence[-1], "%Y-%m-%d") + datetime.timedelta(days=3)).strftime("%Y-%m-%d")
|
||||
stop_update_period = (datetime.datetime.strptime(
|
||||
today, "%Y-%m-%d") - datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d")).days
|
||||
|
||||
# 保存频度 指标名称 分类 指标id 到 df
|
||||
df2 = pd.DataFrame({'指标分类': ClassifyName, '指标名称': EdbName, '指标id': EdbCode, '频度': Frequency,'指标来源':SourceName,'来源id':Source,'最后更新时间':edbname_df['DataTime'].values[-1],'更新周期':most_common_weekday,'预警日期':warning_date,'停更周期':stop_update_period},index=[0])
|
||||
df2 = pd.DataFrame({'指标分类': ClassifyName, '指标名称': EdbName, '指标id': EdbCode, '频度': Frequency, '指标来源': SourceName, '来源id': Source,
|
||||
'最后更新时间': edbname_df['DataTime'].values[-1], '更新周期': most_common_weekday, '预警日期': warning_date, '停更周期': stop_update_period}, index=[0])
|
||||
|
||||
# df = pd.merge(df, df2, how='outer')
|
||||
df = pd.concat([df, df2])
|
||||
@ -388,7 +399,8 @@ class EtaReader():
|
||||
logger.info(f'跳过指标 {EdbName}')
|
||||
|
||||
# 找到列表中不在指标列中的指标id，保存成新的list
|
||||
new_list = [item for item in self.edbcodelist if item not in df['指标id'].tolist()]
|
||||
new_list = [
|
||||
item for item in self.edbcodelist if item not in df['指标id'].tolist()]
|
||||
logger.info(new_list)
|
||||
# 遍历new_list，获取指标数据，保存到df1
|
||||
for item in new_list:
|
||||
@ -399,33 +411,196 @@ class EtaReader():
|
||||
except:
|
||||
itemname = item
|
||||
|
||||
df1 = self.edbcodegetdata(df1,item,itemname)
|
||||
df = pd.concat([df, pd.DataFrame({'指标分类': '其他', '指标名称': itemname, '指标id': item, '频度': '其他','指标来源':'其他','来源id':'其他'},index=[0])])
|
||||
df1 = self.edbcodegetdata(df1, item, itemname)
|
||||
df = pd.concat([df, pd.DataFrame(
|
||||
{'指标分类': '其他', '指标名称': itemname, '指标id': item, '频度': '其他', '指标来源': '其他', '来源id': '其他'}, index=[0])])
|
||||
|
||||
# 按时间排序
|
||||
df1.sort_values('DataTime',inplace=True,ascending=False)
|
||||
df1.rename(columns={'DataTime': 'date'},inplace=True)
|
||||
df1.sort_values('DataTime', inplace=True, ascending=False)
|
||||
df1.rename(columns={'DataTime': 'date'}, inplace=True)
|
||||
# df1.dropna(inplace=True)
|
||||
# 去掉大于今天日期的行
|
||||
df1 = df1[df1['date'] <= datetime.datetime.now().strftime('%Y-%m-%d')]
|
||||
logger.info(df1.head())
|
||||
# logger.info(f'{df1.head()}')
|
||||
# 保存到xlsx文件的sheet表
|
||||
with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
|
||||
with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
|
||||
df1.to_excel(file, sheet_name='指标数据', index=False)
|
||||
df.to_excel(file, sheet_name='指标列表', index=False)
|
||||
df_zhibiaoshuju = df1.copy()
|
||||
df_zhibiaoliebiao = df.copy()
|
||||
return df_zhibiaoshuju,df_zhibiaoliebiao
|
||||
return df_zhibiaoshuju, df_zhibiaoliebiao
|
||||
|
||||
def get_eta_api_pp_data(self,data_set,dataset=''):
|
||||
def get_eta_api_chengpinyou_data(self, data_set, dataset=''):
|
||||
'''
|
||||
从ETA API获取原油数据
|
||||
|
||||
参数:
|
||||
data_set (str): 数据集名称
|
||||
dataset (str): 数据集ID，默认为空
|
||||
|
||||
返回:
|
||||
None
|
||||
'''
|
||||
today = datetime.date.today().strftime("%Y-%m-%d")
|
||||
|
||||
# 定义你的headers，这里可以包含多个参数
|
||||
self.headers = {
|
||||
'nonce': self.signature.nonce, # 例如，一个认证令牌
|
||||
# 自定义的header参数
|
||||
'timestamp': str(self.signature.timestamp),
|
||||
'appid': self.signature.APPID, # 另一个自定义的header参数
|
||||
'signature': self.signature.signature
|
||||
}
|
||||
|
||||
# 从列表数据中获取指标名称，判断指标名称频度是否为日 ，如果是，则获取UniqueCode，然后获取指标数据，保存到xlat文件中的sheet表。
|
||||
|
||||
'''
|
||||
df = sheetname 指标列表，存储 指标分类-指标名称-指标id-频度
|
||||
df1 = sheetname 指标数据 ,存储 时间-指标名称1-指标名称2...
|
||||
|
||||
'''
|
||||
|
||||
# 构建新的DataFrame df df1
|
||||
df = pd.DataFrame(columns=[
|
||||
'指标分类', '指标名称', '指标id', '频度', '指标来源', '来源id', '最后更新时间', '更新周期', '预警日期', '停更周期'])
|
||||
df1 = pd.DataFrame(columns=['DataTime'])
|
||||
|
||||
# 外网环境无法访问，请确认是否为内网环境
|
||||
try:
|
||||
# 发送GET请求 获取指标分类列表
|
||||
response = requests.get(self.classifylisturl, headers=self.headers)
|
||||
except requests.exceptions.RequestException as e:
|
||||
raise Exception(f"请求失败,请确认是否为内网环境: {e}", "\033[0m")
|
||||
|
||||
# 检查响应状态码
|
||||
if response.status_code == 200:
|
||||
# 获取成功， 处理响应内容
|
||||
data = response.json() # 假设接口返回的是JSON数据
|
||||
|
||||
# 请求成功，处理响应内容
|
||||
# logger.info(data.get('Data'))
|
||||
# 定义你想要保留的固定值
|
||||
# fixed_value = 1193 # 成品油
|
||||
fixed_value = 1285 # 沥青
|
||||
|
||||
# 遍历列表，只保留那些'category' key的值为固定值的数据项
|
||||
filtered_data = [item for item in data.get(
|
||||
'Data') if item.get('ParentId') == fixed_value]
|
||||
|
||||
# 然后循环filtered_data去获取list数据，才能获取到想要获取的ClassifyId
|
||||
n = 0
|
||||
for item in filtered_data:
|
||||
n += 1
|
||||
# if n>50:
|
||||
# break
|
||||
ClassifyId = item["ClassifyId"] # 分类id，分类下的指标列表接口的请求参数
|
||||
ClassifyName = item["ClassifyName"] # 分类名称，要保存到df的指标分类列
|
||||
# 根据分类id，获取指标列表
|
||||
url = self.classifyidlisturl+str(ClassifyId)
|
||||
response = requests.get(url, headers=self.headers)
|
||||
if response.status_code == 200:
|
||||
# logger.info(response.text)
|
||||
data2 = response.json()
|
||||
Data = data2.get('Data')
|
||||
for i in Data:
|
||||
# s+= 1
|
||||
EdbCode = i.get('EdbCode')
|
||||
# 指标名称，要保存到df2的指标名称列,df的指标名称列
|
||||
EdbName = i.get('EdbName')
|
||||
Frequency = i.get('Frequency') # 频度，要保存到df的频度列
|
||||
SourceName = i.get('SourceName') # 来源名称，要保存到df的频度列
|
||||
Source = i.get('Source') # 来源ID，要保存到df的频度列
|
||||
|
||||
# 保存到df
|
||||
df1 = self.edbcodegetdata(df1, EdbCode, EdbName)
|
||||
# 取df1所有行最后一列
|
||||
edbname_df = df1[['DataTime', f'{EdbName}']]
|
||||
edbname_df = edbname_df.dropna()
|
||||
|
||||
if len(edbname_df) == 0:
|
||||
logger.info(f'指标名称：{EdbName} 没有数据')
|
||||
continue
|
||||
try:
|
||||
time_sequence = edbname_df['DataTime'].values.tolist(
|
||||
)[-10:]
|
||||
except IndexError:
|
||||
time_sequence = edbname_df['DataTime'].values.tolist(
|
||||
)
|
||||
# 使用Counter来统计每个星期几的出现次数
|
||||
from collections import Counter
|
||||
weekday_counter = Counter(datetime.datetime.strptime(
|
||||
time_str, "%Y-%m-%d").strftime('%A') for time_str in time_sequence)
|
||||
|
||||
# 打印出现次数最多的星期几
|
||||
try:
|
||||
most_common_weekday = weekday_counter.most_common(1)[
|
||||
0][0]
|
||||
# 计算两周后的日期
|
||||
warning_date = (datetime.datetime.strptime(
|
||||
time_sequence[-1], "%Y-%m-%d") + datetime.timedelta(weeks=2)).strftime("%Y-%m-%d")
|
||||
stop_update_period = (datetime.datetime.strptime(
|
||||
today, "%Y-%m-%d") - datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d")).days // 7
|
||||
|
||||
except IndexError:
|
||||
most_common_weekday = '其他'
|
||||
stop_update_period = 0
|
||||
if '日' in Frequency:
|
||||
most_common_weekday = '每天'
|
||||
warning_date = (datetime.datetime.strptime(
|
||||
time_sequence[-1], "%Y-%m-%d") + datetime.timedelta(days=3)).strftime("%Y-%m-%d")
|
||||
stop_update_period = (datetime.datetime.strptime(
|
||||
today, "%Y-%m-%d") - datetime.datetime.strptime(time_sequence[-1], "%Y-%m-%d")).days
|
||||
|
||||
# 保存频度 指标名称 分类 指标id 到 df
|
||||
df2 = pd.DataFrame({'指标分类': ClassifyName, '指标名称': EdbName, '指标id': EdbCode, '频度': Frequency, '指标来源': SourceName, '来源id': Source,
|
||||
'最后更新时间': edbname_df['DataTime'].values[-1], '更新周期': most_common_weekday, '预警日期': warning_date, '停更周期': stop_update_period}, index=[0])
|
||||
|
||||
# df = pd.merge(df, df2, how='outer')
|
||||
df = pd.concat([df, df2])
|
||||
|
||||
# 找到列表中不在指标列中的指标id，保存成新的list
|
||||
new_list = [
|
||||
item for item in self.edbcodelist if item not in df['指标id'].tolist()]
|
||||
logger.info(new_list)
|
||||
# 遍历new_list，获取指标数据，保存到df1
|
||||
for item in new_list:
|
||||
logger.info(item)
|
||||
# 将item 加入到 df['指标id']中
|
||||
try:
|
||||
itemname = edbcodenamedict[item]
|
||||
except:
|
||||
itemname = item
|
||||
|
||||
df1 = self.edbcodegetdata(df1, item, itemname)
|
||||
df = pd.concat([df, pd.DataFrame(
|
||||
{'指标分类': '其他', '指标名称': itemname, '指标id': item, '频度': '其他', '指标来源': '其他', '来源id': '其他'}, index=[0])])
|
||||
|
||||
# 按时间排序
|
||||
df1.sort_values('DataTime', inplace=True, ascending=False)
|
||||
df1.rename(columns={'DataTime': 'date'}, inplace=True)
|
||||
# df1.dropna(inplace=True)
|
||||
# 去掉大于今天日期的行
|
||||
df1 = df1[df1['date'] <= datetime.datetime.now().strftime('%Y-%m-%d')]
|
||||
logger.info(df1.head())
|
||||
# logger.info(f'{df1.head()}')
|
||||
# 保存到xlsx文件的sheet表
|
||||
with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
|
||||
df1.to_excel(file, sheet_name='指标数据', index=False)
|
||||
df.to_excel(file, sheet_name='指标列表', index=False)
|
||||
df_zhibiaoshuju = df1.copy()
|
||||
df_zhibiaoliebiao = df.copy()
|
||||
return df_zhibiaoshuju, df_zhibiaoliebiao
|
||||
|
||||
def get_eta_api_pp_data(self, data_set, dataset=''):
|
||||
global ClassifyId
|
||||
today = datetime.date.today().strftime("%Y-%m-%d")
|
||||
|
||||
# 定义你的headers，这里可以包含多个参数
|
||||
self.headers = {
|
||||
'nonce': self.signature.nonce, # 例如，一个认证令牌
|
||||
'timestamp': str(self.signature.timestamp), # 自定义的header参数
|
||||
# 自定义的header参数
|
||||
'timestamp': str(self.signature.timestamp),
|
||||
'appid': self.signature.APPID, # 另一个自定义的header参数
|
||||
'signature': self.signature.signature
|
||||
}
|
||||
@ -442,13 +617,12 @@ class EtaReader():
|
||||
df = pd.DataFrame(columns=['指标分类', '指标名称', '指标id', '频度'])
|
||||
df1 = pd.DataFrame(columns=['DataTime'])
|
||||
|
||||
|
||||
# 外网环境无法访问，请确认是否为内网环境
|
||||
try:
|
||||
# 发送GET请求 获取指标分类列表
|
||||
response = requests.get(self.classifylisturl, headers=self.headers)
|
||||
except requests.exceptions.RequestException as e:
|
||||
raise Exception(f"请求失败,请确认是否为内网环境: {e}","\033[0m")
|
||||
raise Exception(f"请求失败,请确认是否为内网环境: {e}", "\033[0m")
|
||||
|
||||
# 检查响应状态码
|
||||
if response.status_code == 200:
|
||||
@ -461,16 +635,17 @@ class EtaReader():
|
||||
fixed_value = ClassifyId
|
||||
|
||||
# 遍历列表，只保留那些'category' key的值为固定值的数据项
|
||||
filtered_data = [item for item in data.get('Data') if item.get('ParentId') == fixed_value]
|
||||
filtered_data = [item for item in data.get(
|
||||
'Data') if item.get('ParentId') == fixed_value]
|
||||
|
||||
#然后循环filtered_data去获取list数据，才能获取到想要获取的ClassifyId
|
||||
# 然后循环filtered_data去获取list数据，才能获取到想要获取的ClassifyId
|
||||
n = 0
|
||||
for item in filtered_data:
|
||||
n+= 1
|
||||
n += 1
|
||||
# if n>50:
|
||||
# break
|
||||
ClassifyId = item["ClassifyId"] #分类id，分类下的指标列表接口的请求参数
|
||||
ClassifyName = item["ClassifyName"] #分类名称，要保存到df的指标分类列
|
||||
ClassifyId = item["ClassifyId"] # 分类id，分类下的指标列表接口的请求参数
|
||||
ClassifyName = item["ClassifyName"] # 分类名称，要保存到df的指标分类列
|
||||
# 根据分类id，获取指标列表
|
||||
url = self.classifyidlisturl+str(ClassifyId)
|
||||
response = requests.get(url, headers=self.headers)
|
||||
@ -481,27 +656,30 @@ class EtaReader():
|
||||
for i in Data:
|
||||
# s+= 1
|
||||
EdbCode = i.get('EdbCode')
|
||||
EdbName = i.get('EdbName') # 指标名称，要保存到df2的指标名称列,df的指标名称列
|
||||
# 指标名称，要保存到df2的指标名称列,df的指标名称列
|
||||
EdbName = i.get('EdbName')
|
||||
Frequency = i.get('Frequency') # 频度，要保存到df的频度列
|
||||
# 频度不是 日 或者 周的 跳过
|
||||
if Frequency not in ['日度','周度','日','周']:
|
||||
if Frequency not in ['日度', '周度', '日', '周']:
|
||||
continue
|
||||
|
||||
# 判断名称是否需要保存
|
||||
isSave = self.filter_pp_data(ClassifyName,EdbName)
|
||||
isSave = self.filter_pp_data(ClassifyName, EdbName)
|
||||
if isSave:
|
||||
# 保存到df
|
||||
# 保存频度 指标名称 分类 指标id 到 df
|
||||
df2 = pd.DataFrame({'指标分类': ClassifyName, '指标名称': EdbName, '指标id': EdbCode, '频度': Frequency},index=[0])
|
||||
df2 = pd.DataFrame(
|
||||
{'指标分类': ClassifyName, '指标名称': EdbName, '指标id': EdbCode, '频度': Frequency}, index=[0])
|
||||
|
||||
# df = pd.merge(df, df2, how='outer')
|
||||
df = pd.concat([df, df2])
|
||||
df1 = self.edbcodegetdata(df1,EdbCode,EdbName)
|
||||
df1 = self.edbcodegetdata(df1, EdbCode, EdbName)
|
||||
else:
|
||||
logger.info(f'跳过指标 {EdbName}')
|
||||
|
||||
# 找到列表中不在指标列中的指标id，保存成新的list
|
||||
new_list = [item for item in self.edbcodelist if item not in df['指标id'].tolist()]
|
||||
new_list = [
|
||||
item for item in self.edbcodelist if item not in df['指标id'].tolist()]
|
||||
logger.info(new_list)
|
||||
# 遍历new_list，获取指标数据，保存到df1
|
||||
for item in new_list:
|
||||
@ -512,41 +690,44 @@ class EtaReader():
|
||||
except:
|
||||
itemname = item
|
||||
|
||||
df1 = self.edbcodegetdata(df1,item,itemname)
|
||||
df = pd.concat([df, pd.DataFrame({'指标分类': '其他', '指标名称': itemname, '指标id': item, '频度': '其他'},index=[0])])
|
||||
df1 = self.edbcodegetdata(df1, item, itemname)
|
||||
df = pd.concat([df, pd.DataFrame(
|
||||
{'指标分类': '其他', '指标名称': itemname, '指标id': item, '频度': '其他'}, index=[0])])
|
||||
|
||||
# 按时间排序
|
||||
df1.sort_values('DataTime',inplace=True,ascending=False)
|
||||
df1.rename(columns={'DataTime': 'date'},inplace=True)
|
||||
df1.sort_values('DataTime', inplace=True, ascending=False)
|
||||
df1.rename(columns={'DataTime': 'date'}, inplace=True)
|
||||
# df1.dropna(inplace=True)
|
||||
# 去掉大于今天日期的行
|
||||
df1 = df1[df1['date'] <= datetime.datetime.now().strftime('%Y-%m-%d')]
|
||||
logger.info(df1.head())
|
||||
# logger.info(f'{df1.head()}')
|
||||
# 保存到xlsx文件的sheet表
|
||||
with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
|
||||
with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
|
||||
df1.to_excel(file, sheet_name='指标数据', index=False)
|
||||
df.to_excel(file, sheet_name='指标列表', index=False)
|
||||
|
||||
df_zhibiaoshuju = df1.copy()
|
||||
df_zhibiaoliebiao = df.copy()
|
||||
return df_zhibiaoshuju,df_zhibiaoliebiao
|
||||
return df_zhibiaoshuju, df_zhibiaoliebiao
|
||||
|
||||
def push_data(self,data):
|
||||
def push_data(self, data):
|
||||
|
||||
today = datetime.date.today().strftime("%Y-%m-%d")
|
||||
|
||||
# 定义你的headers，这里可以包含多个参数
|
||||
self.headers = {
|
||||
'nonce': self.signature.nonce, # 例如，一个认证令牌
|
||||
'timestamp': str(self.signature.timestamp), # 自定义的header参数
|
||||
# 自定义的header参数
|
||||
'timestamp': str(self.signature.timestamp),
|
||||
'appid': self.signature.APPID, # 另一个自定义的header参数
|
||||
'signature': self.signature.signature
|
||||
}
|
||||
|
||||
# 发送post请求 上传数据
|
||||
logger.info('请求参数：',data)
|
||||
response = requests.post(self.edbdatapushurl, headers=self.headers,data=json.dumps(data))
|
||||
logger.info('请求参数：', data)
|
||||
response = requests.post(
|
||||
self.edbdatapushurl, headers=self.headers, data=json.dumps(data))
|
||||
|
||||
# 检查响应状态码
|
||||
if response.status_code == 200:
|
||||
@ -560,23 +741,24 @@ class EtaReader():
|
||||
# 主动抛出异常
|
||||
raise Exception(f'Error: {response.status_code}, {response.text}')
|
||||
|
||||
def del_zhibiao(self,IndexCodeList):
|
||||
def del_zhibiao(self, IndexCodeList):
|
||||
today = datetime.date.today().strftime("%Y-%m-%d")
|
||||
|
||||
# 定义你的headers，这里可以包含多个参数
|
||||
self.headers = {
|
||||
'nonce': self.signature.nonce, # 例如，一个认证令牌
|
||||
'timestamp': str(self.signature.timestamp), # 自定义的header参数
|
||||
# 自定义的header参数
|
||||
'timestamp': str(self.signature.timestamp),
|
||||
'appid': self.signature.APPID, # 另一个自定义的header参数
|
||||
'signature': self.signature.signature
|
||||
}
|
||||
|
||||
data = {
|
||||
"IndexCodeList": IndexCodeList #指标编码列表
|
||||
}
|
||||
"IndexCodeList": IndexCodeList # 指标编码列表
|
||||
}
|
||||
# 发送post请求 上传数据
|
||||
response = requests.post(self.edbdeleteurl, headers=self.headers,data=json.dumps(data))
|
||||
|
||||
response = requests.post(
|
||||
self.edbdeleteurl, headers=self.headers, data=json.dumps(data))
|
||||
|
||||
# 检查响应状态码
|
||||
if response.status_code == 200:
|
||||
@ -590,7 +772,7 @@ class EtaReader():
|
||||
# 主动抛出异常
|
||||
raise Exception(f'Error: {response.status_code}, {response.text}')
|
||||
|
||||
def del_business(self,data):
|
||||
def del_business(self, data):
|
||||
''''
|
||||
接口地址
|
||||
https://console-docs.apipost.cn/preview/fce869601d0be1d9/9a637c2f9ed0c589?target_id=d3cafcbf-a68c-42b3-b105-7bbd0e95a9cd
|
||||
@ -607,15 +789,15 @@ class EtaReader():
|
||||
# 定义你的headers，这里可以包含多个参数
|
||||
self.headers = {
|
||||
'nonce': self.signature.nonce, # 例如，一个认证令牌
|
||||
'timestamp': str(self.signature.timestamp), # 自定义的header参数
|
||||
# 自定义的header参数
|
||||
'timestamp': str(self.signature.timestamp),
|
||||
'appid': self.signature.APPID, # 另一个自定义的header参数
|
||||
'signature': self.signature.signature
|
||||
}
|
||||
|
||||
|
||||
# 发送post请求 上传数据
|
||||
response = requests.post(self.edbbusinessurl, headers=self.headers,data=json.dumps(data))
|
||||
|
||||
response = requests.post(
|
||||
self.edbbusinessurl, headers=self.headers, data=json.dumps(data))
|
||||
|
||||
# 检查响应状态码
|
||||
if response.status_code == 200:
|
||||
@ -634,6 +816,7 @@ class BinanceAPI:
|
||||
'''
|
||||
获取 Binance API 请求头签名
|
||||
'''
|
||||
|
||||
def __init__(self, APPID, SECRET):
|
||||
self.APPID = APPID
|
||||
self.SECRET = SECRET
|
||||
@ -641,7 +824,8 @@ class BinanceAPI:
|
||||
|
||||
# 生成随机字符串作为 nonce
|
||||
def generate_nonce(self, length=32):
|
||||
self.nonce = ''.join(random.choices(string.ascii_letters + string.digits, k=length))
|
||||
self.nonce = ''.join(random.choices(
|
||||
string.ascii_letters + string.digits, k=length))
|
||||
return self.nonce
|
||||
|
||||
# 获取当前时间戳（秒）
|
||||
@ -664,7 +848,8 @@ class BinanceAPI:
|
||||
self.signature = self.calculate_signature(self.SECRET, self.sign_str)
|
||||
# return self.signature
|
||||
|
||||
### 日志配置
|
||||
# 日志配置
|
||||
|
||||
|
||||
# 创建日志目录（如果不存在）
|
||||
log_dir = 'logs'
|
||||
@ -676,8 +861,10 @@ logger = logging.getLogger('pricepredict')
|
||||
logger.setLevel(logging.INFO)
|
||||
|
||||
# 配置文件处理器，将日志记录到文件
|
||||
file_handler = logging.handlers.RotatingFileHandler(os.path.join(log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
|
||||
file_handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
|
||||
file_handler = logging.handlers.RotatingFileHandler(os.path.join(
|
||||
log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
|
||||
file_handler.setFormatter(logging.Formatter(
|
||||
'%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
|
||||
|
||||
# 配置控制台处理器，将日志打印到控制台
|
||||
console_handler = logging.StreamHandler()
|
||||
@ -688,7 +875,6 @@ logger.addHandler(file_handler)
|
||||
logger.addHandler(console_handler)
|
||||
|
||||
|
||||
|
||||
# eta 接口url
|
||||
sourcelisturl = 'http://10.189.2.78:8108/v1/edb/source/list'
|
||||
classifylisturl = 'http://10.189.2.78:8108/v1/edb/classify/list?ClassifyType='
|
||||
@ -698,47 +884,48 @@ edbcodedataurl = 'http://10.189.2.78:8108/v1/edb/data?EdbCode='
|
||||
edbdatapushurl = 'http://10.189.2.78:8108/v1/edb/push'
|
||||
edbdeleteurl = 'http://10.189.2.78:8108/v1/edb/business/edb/del'
|
||||
edbbusinessurl = 'http://10.189.2.78:8108/v1/edb/business/data/del'
|
||||
edbcodelist = ['CO1 Comdty', 'ovx index', 'C2404194834', 'C2404199738', 'dxy curncy', 'C2403128043', 'C2403150124',
|
||||
'DOESCRUD Index', 'WTRBM1 EEGC Index', 'FVHCM1 INDEX', 'doedtprd index', 'CFFDQMMN INDEX',
|
||||
'C2403083739', 'C2404167878', 'C2403250571', 'lmcads03 lme comdty', 'GC1 COMB Comdty',
|
||||
'C2404171822','C2404167855',
|
||||
# 'W000825','W000826','G.IPE', # 美国汽柴油
|
||||
# 'S5131019','ID00135604','FSGAM1 Index','S5120408','ID00136724', # 新加坡汽柴油
|
||||
]
|
||||
edbcodelist = [
|
||||
# 'CO1 Comdty', 'ovx index', 'C2404194834', 'C2404199738', 'dxy curncy', 'C2403128043', 'C2403150124',
|
||||
# 'DOESCRUD Index', 'WTRBM1 EEGC Index', 'FVHCM1 INDEX', 'doedtprd index', 'CFFDQMMN INDEX',
|
||||
# 'C2403083739', 'C2404167878', 'C2403250571', 'lmcads03 lme comdty', 'GC1 COMB Comdty',
|
||||
# 'C2404171822', 'C2404167855',
|
||||
# 'W000825','W000826','G.IPE', # 美国汽柴油
|
||||
# 'S5131019','ID00135604','FSGAM1 Index','S5120408','ID00136724', # 新加坡汽柴油
|
||||
]
|
||||
|
||||
|
||||
# eta自有数据指标编码
|
||||
modelsindex = {
|
||||
'NHITS': 'SELF0000001',
|
||||
'Informer':'SELF0000057',
|
||||
'LSTM':'SELF0000058',
|
||||
'iTransformer':'SELF0000059',
|
||||
'TSMixer':'SELF0000060',
|
||||
'TSMixerx':'SELF0000061',
|
||||
'PatchTST':'SELF0000062',
|
||||
'RNN':'SELF0000063',
|
||||
'GRU':'SELF0000064',
|
||||
'TCN':'SELF0000065',
|
||||
'BiTCN':'SELF0000066',
|
||||
'DilatedRNN':'SELF0000067',
|
||||
'MLP':'SELF0000068',
|
||||
'DLinear':'SELF0000069',
|
||||
'NLinear':'SELF0000070',
|
||||
'TFT':'SELF0000071',
|
||||
'FEDformer':'SELF0000072',
|
||||
'StemGNN':'SELF0000073',
|
||||
'MLPMultivariate':'SELF0000074',
|
||||
'TiDE':'SELF0000075',
|
||||
'DeepNPTS':'SELF0000076'
|
||||
}
|
||||
# 'NHITS': 'SELF0000001',
|
||||
# 'Informer': 'SELF0000057',
|
||||
# 'LSTM': 'SELF0000058',
|
||||
# 'iTransformer': 'SELF0000059',
|
||||
# 'TSMixer': 'SELF0000060',
|
||||
# 'TSMixerx': 'SELF0000061',
|
||||
# 'PatchTST': 'SELF0000062',
|
||||
# 'RNN': 'SELF0000063',
|
||||
# 'GRU': 'SELF0000064',
|
||||
# 'TCN': 'SELF0000065',
|
||||
# 'BiTCN': 'SELF0000066',
|
||||
# 'DilatedRNN': 'SELF0000067',
|
||||
# 'MLP': 'SELF0000068',
|
||||
# 'DLinear': 'SELF0000069',
|
||||
# 'NLinear': 'SELF0000070',
|
||||
# 'TFT': 'SELF0000071',
|
||||
# 'FEDformer': 'SELF0000072',
|
||||
# 'StemGNN': 'SELF0000073',
|
||||
# 'MLPMultivariate': 'SELF0000074',
|
||||
# 'TiDE': 'SELF0000075',
|
||||
# 'DeepNPTS': 'SELF0000076'
|
||||
}
|
||||
|
||||
edbcodelist = edbcodelist+list(modelsindex.values())
|
||||
|
||||
### 文件
|
||||
data_set = '历史预测结果.xlsx' # 数据集文件
|
||||
# 文件
|
||||
data_set = '沥青eta数据.xlsx' # 数据集文件
|
||||
# data_set = 'INE_OIL(1).csv'
|
||||
### 文件夹
|
||||
dataset = 'yuanyoudataset' # 数据集文件夹
|
||||
# 文件夹
|
||||
dataset = 'yuanyoudataset' # 数据集文件夹
|
||||
|
||||
|
||||
# eta 接口token
|
||||
@ -754,4 +941,10 @@ etadata = EtaReader(signature=signature,
|
||||
edbdeleteurl=edbdeleteurl,
|
||||
edbbusinessurl=edbbusinessurl,
|
||||
)
|
||||
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_chengpinyou_data(
|
||||
data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||
|
||||
# 保存到xlsx文件的sheet表
|
||||
with pd.ExcelWriter(os.path.join(data_set)) as file:
|
||||
df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
|
||||
df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
||||
|
||||
BIN
成品油eta数据.xlsx
Normal file
BIN
成品油eta数据.xlsx
Normal file
Binary file not shown.
BIN
沥青eta数据.xlsx
Normal file
BIN
沥青eta数据.xlsx
Normal file
Binary file not shown.
Loading…
Reference in New Issue
Block a user