石油焦普货周度月度配置
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workpc
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config_shiyoujiao_puhuo_yuedu.py
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619
config_shiyoujiao_puhuo_yuedu.py
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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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edbcodenamedict = {
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'C2403283369': '预赔阳极加工利润（高端）',
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'C2403285560': '预培阳极加工利润（低端）',
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'C2403288616': '低硫石油焦煅烧利润',
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'S6949656': '平均价：氧化铝：一级：全国',
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'S5807052': '氧化铝：一级：贵阳',
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'S5443355': '市场价：煤沥青：河北地区',
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'S5443357': '市场价：煤沥青：山西地区',
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'W000294': '国内主要港口石油焦出货量（隆重）',
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'W000293': '日照港库存（隆重）',
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'W000292': '港口总库存（隆重）',
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'W000283': '主营石油焦产量（隆重）',
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'W000282': '地炼石油焦产量（隆重）',
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'W000281': '中国石油焦产量（隆重）',
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'W000280': '主营石油焦开工负荷率（隆重）',
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'W000279': '地炼石油焦开工负荷率（隆重）',
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'ID00150273': '石油焦：1 # ：市场低端价：东北地区（日）',
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'ID00150281': '石油焦：1 # ：市场主流价：东北地区（日）',
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'ID00150277': '石油焦：1 # ：市场高端价：东北地区（日）',
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'ID00150289': '石油焦：2 # ：市场低端价：华东地区（日）',
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'ID00150285': '石油焦：2 # ：市场低端价：西北地区（日）',
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'ID00150313': '石油焦：2 # ：市场主流价：华东地区（日）',
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'ID00150309': '石油焦：2 # ：市场主流价：西北地区（日）',
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'ID00150301': '石油焦：2 # ：市场高端价：华东地区（日）',
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'ID00150297': '石油焦：2 # ：市场高端价：西北地区（日）',
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'ID00150321': '石油焦：2 # A：市场低端价：山东（日）',
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'ID00150329': '石油焦：2 # A：市场主流价：山东（日）',
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'ID00150325': '石油焦：2 # A：市场高端价：山东（日）',
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'ID00150337': '石油焦：2 # B：市场低端价：华南地区（日）',
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'ID00150341': '石油焦：2 # B：市场低端价：华中地区（日）',
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'ID00150361': '石油焦：2 # B：市场主流价：华南地区（日）',
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'ID00150365': '石油焦：2 # B：市场主流价：华中地区（日）',
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'ID00150349': '石油焦：2 # B：市场高端价：华南地区（日）',
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'ID00150353': '石油焦：2 # B：市场高端价：华中地区（日）',
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'ID00150333': '石油焦：2 # B：市场低端价：山东（日）',
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'ID00150357': '石油焦：2 # B：市场主流价：山东（日）',
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'ID00150345': '石油焦：2 # B：市场高端价：山东（日）',
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'ID00150369': '石油焦：3 # ：市场低端价：华中地区（日）',
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'ID00150373': '石油焦：3 # ：市场高端价：华中地区（日）',
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'ID00150385': '石油焦：3 # A：市场高端价：山东（日）',
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'ID00150393': '石油焦：3 # B：市场低端价：华东地区（日）',
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'ID00150409': '石油焦：3 # B：市场主流价：华东地区（日）',
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'ID00150401': '石油焦：3 # B：市场高端价：华东地区（日）',
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'ID00146589': '海绵焦：4 # ：出厂价：华中地区：洛阳石化（日）',
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'ID01242846': '石油焦：4 # B：挂牌价：华北地区：中石化燕山（日）',
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'ID01300358': '石油焦：3 # C：市场低端价：山东（日）',
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'ID01300357': '石油焦：3 # C：市场高端价：山东（日）',
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'ID00150377': '石油焦：3 # ：市场主流价：华中地区（日）',
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'ID01387643': '煅烧焦：低硫：0.5 % S：市场价：东北地区（日）',
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'ID01387646': '煅烧焦：低硫：3.5 % S：市场价：东北地区（日）',
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'ID01387660': '煅烧焦：中硫：3 % S，400V：市场价：山东（日）',
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'ID00150381': '石油焦：3 # A：市场低端价：山东（日）',
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'ID00150397': '石油焦：3 # B：市场低端价：山东（日）',
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'ID00150405': '石油焦：3 # B：市场高端价：山东（日）',
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'ID00146545': '海绵焦：3B：出厂价：山东：山东东明（日）',
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'B3e90b34e4b9e7a6ea3': '石油焦市场均价(元/吨)',
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'B6b5c53b270a3af12ac': '石油焦1 # 市场均价(元/吨)',
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'B10721189a11c209a20': '石油焦2 # 市场均价(元/吨)',
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'B6accfa9d2bf4735a50': '石油焦3 # 市场均价(元/吨)',
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'B8a0ab5357569c385a9': '石油焦海绵焦市场均价(元/吨)',
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'B19dcf45e22fbfd3e43': '石油焦海绵焦东北1 # A焦（低端）(元/吨)（百川）',
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'B5832a62d1e0fba50b6': '石油焦海绵焦东北1 # A焦（高端）(元/吨)（百川）',
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'B1de4fba026d4609cc7': '石油焦海绵焦东北1 # B焦（低端）(元/吨)（百川）',
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'B38f89180736172490d': '石油焦海绵焦东北1 # B焦（高端）(元/吨)（百川）',
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'B4f847871674c3d77f2': '石油焦海绵焦山东地炼1 # -3#焦（低端）(元/吨)',
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'B1aefb8a64a5200adbd': '石油焦海绵焦山东地炼1 # -3#焦（高端）(元/吨)',
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'B1df7d0afbfedfb628a': '煅烧焦东北低硫(高端S < 0.5)(元/吨)',
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'B5f8f9859635876da28': '煅烧焦东北低硫(低端S < 0.5)(元/吨)',
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'B2342a8c5a39fa00348': '煅烧焦华北中硫(高端S < 3.0，钒 < 400)(元/吨)',
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'B051f27900397c6a35f': '煅烧焦山东中硫(高端S < 3.0，钒 < 400)(元/吨)',
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'Be2a8050a48e86cae1f': '煅烧焦华东中硫(高端S < 3.0，钒 < 400)(元/吨)',
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'B4a1811938f85065f6a': '煅烧焦华中中硫(高端S < 3.0，钒 < 400)(元/吨)',
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'Bc197d4834ef7fb98ec': '煅烧焦华东高硫(高端S < 3.5，钒 < 400)(元/吨)',
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'B62be5dbdb8c6454530': '煅烧焦低硫参考价格(元/吨)（百川）',
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'Bdd813140bffc4edfa6': '煅烧焦中硫微量市场均价(元/吨)（百川）',
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'B185a597decfc71915a': '预焙阳极山东低端(元/吨)（百川）',
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'B1bcde6130de031bd42': '山西 改质沥青(元/吨)',
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'Bb9f4a1f6dd32b4ad8a': '山东 改质沥青(元/吨)',
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'C2411261557491549': '石油焦市场均价(元/吨)/4DMA',
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'C2411271143174617': '石油焦市场均价(元/吨)/9DMA',
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'ID01387649': '煅烧焦：中硫：3 % S，350V：市场价：华东地区（日）',
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'ID01387655': '煅烧焦：中硫：3 % S，350V：市场价：山东（日）',
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'RE00010076': '煅烧焦：低硫：生产毛利：东北地区（周）',
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'B9d1acaf80383683da3': '石油焦总产量（周）(吨)',
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'Bdaa719a38936c8dd76': '石油焦开工率（周）( % )',
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'B9459d549a332b200e7': '石油焦行业总库存（周）(吨)',
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'Bce6e098b9518370cff': '石油焦工厂库存（周）(吨)',
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'B577ce2809772779710': '石油焦市场库存（周）(吨)',
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'B5d8c564c62f3e6b77f': '石油焦成本（周）(吨)',
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'B43baa98bcaa06c11a5': '石油焦利润（周）(吨)',
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'Bdd0c1361d94081211c': '煅烧石油焦总产量（周）(吨)',
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'B65315111fa28951b1e': '煅烧石油焦开工率（周）( % )',
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'B2aff5f2632a20027d0': '煅烧石油焦行业总库存（周）(吨)',
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'B29fbd31128cd71b212': '煅烧石油焦工厂库存（周）(吨)',
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'B7a88313a89d1261c53': '煅烧石油焦成本（周）(吨)',
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'Bd4fa36b4decec0aafa': '煅烧石油焦利润（周）(吨)',
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'B9bd80eac7df81ffbd4': '预焙阳极总产量（周）(吨)',
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'B27074786605f4660d2': '预焙阳极开工率（周）( % )',
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'Bdc2a5985ecb56b6a0c': '预焙阳极行业总库存（周）(吨)',
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'Bce8511f899e487e5b6': '预焙阳极工厂库存（周）(吨)',
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'B13ec89105bd866a2bd': '预焙阳极成本（周）(吨)',
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'B66c3abcfa15a2e611c': '预焙阳极利润（周）(吨)',
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'Bf7efe3200f9abc0453': '电解铝开工率（周）( % )',
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'Be193166f347267b1a7': '电解铝行业总库存（周）(吨)',
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'Baa744fc97769353175': '电解铝工厂库存（周）(吨)',
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'Bf9654603913cfc5282': '电解铝市场库存（周）(吨)',
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'Bef1535c96da0d70fbc': '电解铝利润（周）(吨)',
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'B7d1d0b24316d49cbdc': '煤沥青总产量（周）(吨)',
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'B4303fb002ea1c214da': '煤沥青开工率（周）( % )',
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'Be9a470c97e9efe660c': '煤沥青行业总库存（周）(吨)',
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'B50d4d87f6b78bca587': '煤沥青工厂库存（周）(吨)',
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'B46cc7d0a90155b5bfd': '煅烧焦山东高硫(高端S < 3.5，普货)(元/吨)'
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}
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edbcodelist = edbcodenamedict.keys()
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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 = ['ds', 'y']+[edbcodenamedict[edbcodename]
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for edbcodename in edbcodelist]
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# eta自有数据指标编码，石油焦普货还没新增，暂且留空
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# eta自有数据指标编码 次周,隔周
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bdwdname = [
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'次月',
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'次二月',
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'次三月',
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'次四月',
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]
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modelsindex = [{"NHITS": "SELF0000451",
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"Informer": "SELF0000452",
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"LSTM": "SELF0000453",
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"iTransformer": "SELF0000454",
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"TSMixer": "SELF0000455",
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"TSMixerx": "SELF0000456",
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"PatchTST": "SELF0000457",
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"RNN": "SELF0000458",
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"GRU": "SELF0000459",
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"TCN": "SELF0000460",
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"BiTCN": "SELF0000461",
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"DilatedRNN": "SELF0000462",
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"MLP": "SELF0000463",
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"DLinear": "SELF0000464",
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"NLinear": "SELF0000465",
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"TFT": "SELF0000466",
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"FEDformer": "SELF0000467",
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"StemGNN": "SELF0000468",
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"MLPMultivariate": "SELF0000469",
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"TiDE": "SELF0000470",
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"DeepNPTS": "SELF0000471",
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"NBEATS": "SELF0000472",
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}, {"NHITS": "SELF0000473",
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"Informer": "SELF0000474",
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"LSTM": "SELF0000475",
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"iTransformer": "SELF0000476",
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"TSMixer": "SELF0000477",
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"TSMixerx": "SELF0000478",
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"PatchTST": "SELF0000479",
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"RNN": "SELF0000480",
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"GRU": "SELF0000481",
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"TCN": "SELF0000482",
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"BiTCN": "SELF0000483",
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"DilatedRNN": "SELF0000484",
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"MLP": "SELF0000485",
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"DLinear": "SELF0000486",
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"NLinear": "SELF0000487",
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"TFT": "SELF0000488",
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"FEDformer": "SELF0000489",
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"StemGNN": "SELF0000490",
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"MLPMultivariate": "SELF0000491",
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"TiDE": "SELF0000492",
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"DeepNPTS": "SELF0000493",
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"NBEATS": "SELF0000494",
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}, {"NHITS": "SELF0000495",
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"Informer": "SELF0000496",
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"LSTM": "SELF0000497",
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"iTransformer": "SELF0000498",
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"TSMixer": "SELF0000499",
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"TSMixerx": "SELF0000500",
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"PatchTST": "SELF0000501",
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"RNN": "SELF0000502",
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"GRU": "SELF0000503",
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"TCN": "SELF0000504",
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"BiTCN": "SELF0000505",
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"DilatedRNN": "SELF0000506",
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"MLP": "SELF0000507",
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"DLinear": "SELF0000508",
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"NLinear": "SELF0000509",
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"TFT": "SELF0000510",
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"FEDformer": "SELF0000511",
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"StemGNN": "SELF0000512",
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"MLPMultivariate": "SELF0000513",
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"TiDE": "SELF0000514",
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"DeepNPTS": "SELF0000515",
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"NBEATS": "SELF0000516",
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}, {"NHITS": "SELF0000517",
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"Informer": "SELF0000518",
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"LSTM": "SELF0000519",
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"iTransformer": "SELF0000520",
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"TSMixer": "SELF0000521",
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"TSMixerx": "SELF0000522",
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"PatchTST": "SELF0000523",
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"RNN": "SELF0000524",
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"GRU": "SELF0000525",
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"TCN": "SELF0000526",
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"BiTCN": "SELF0000527",
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"DilatedRNN": "SELF0000528",
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"MLP": "SELF0000529",
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"DLinear": "SELF0000530",
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"NLinear": "SELF0000531",
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"TFT": "SELF0000532",
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"FEDformer": "SELF0000533",
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"StemGNN": "SELF0000534",
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"MLPMultivariate": "SELF0000535",
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"TiDE": "SELF0000536",
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"DeepNPTS": "SELF0000537",
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"NBEATS": "SELF0000538",
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}]
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# 百川数据指标编码
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baichuanidnamedict = {
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'1588348470396480901': '石油焦滨州-友泰',
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'1588348470396480903': '石油焦东营-海科瑞林',
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'1588348470396480902': '石油焦东营-华联2',
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'1588348470396481080': '石油焦东营-华联3',
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'1588348470396480905': '石油焦东营-联合',
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'1588348470396481081': '石油焦东营-联合3',
|
||||
'1588348470396480915': '石油焦淄博-汇丰',
|
||||
'1588348470396480888': '石油焦沧州-鑫海',
|
||||
'1588348470396480917': '石油焦东营-万通',
|
||||
'1588348470396480925': '石油焦东营-齐润',
|
||||
'1588348470396481084': '石油焦东营-尚能4',
|
||||
'1588348470396480930': '石油焦潍坊-寿光鲁清',
|
||||
'1588348470396480929': '石油焦滨州-鑫岳'
|
||||
}
|
||||
|
||||
|
||||
# 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": "",
|
||||
"IndexName": "石油焦普货价格预测xx模型-yy",
|
||||
"Unit": "无",
|
||||
"Frequency": "日度",
|
||||
"SourceName": f"价格预测",
|
||||
"Remark": 'ddd',
|
||||
"DataList": [
|
||||
{
|
||||
"Date": "2024-05-02",
|
||||
"Value": 333444
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
# eta 分类
|
||||
# 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
|
||||
# url = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId=1214'
|
||||
# ParentId ":1160, 能源化工
|
||||
# ClassifyId ":1214,原油 3912 石油焦
|
||||
# ParentId ":1214,"，就是原油下所有的数据。
|
||||
ClassifyId = 3707
|
||||
|
||||
|
||||
# 变量定义--线上环境
|
||||
# server_host = '10.200.32.39'
|
||||
# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
|
||||
# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
|
||||
# query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# # 上传数据项值
|
||||
# push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
# login_data = {
|
||||
# "data": {
|
||||
# "account": "api_dev",
|
||||
# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
# "terminal": "API"
|
||||
# },
|
||||
# "funcModule": "API",
|
||||
# "funcOperation": "获取token"
|
||||
# }
|
||||
|
||||
|
||||
# upload_data = {
|
||||
# "funcModule":'研究报告信息',
|
||||
# "funcOperation":'上传原油价格预测报告',
|
||||
# "data":{
|
||||
# "groupNo":'', # 用户组id
|
||||
# "ownerAccount":'27663', #报告所属用户账号 27663 - 刘小朋
|
||||
# "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
# "fileName": '', #文件名称
|
||||
# "fileBase64": '' ,#文件内容base64
|
||||
# "categoryNo":'yyjgycbg', # 研究报告分类编码
|
||||
# "smartBusinessClassCode":'YCJGYCBG', #分析报告分类编码
|
||||
# "reportEmployeeCode":"E40482" ,# 报告人 E40482 - 管理员 0000027663 - 刘小朋
|
||||
# "reportDeptCode" :"002000621000", # 报告部门 - 002000621000 SH期货研究部
|
||||
# "productGroupCode":"RAW_MATERIAL" # 商品分类
|
||||
# }
|
||||
# }
|
||||
|
||||
# warning_data = {
|
||||
# "groupNo":'', # 用户组id
|
||||
# "funcModule":'原油特征停更预警',
|
||||
# "funcOperation":'原油特征停更预警',
|
||||
# "data":{
|
||||
# 'WARNING_TYPE_NAME':'特征数据停更预警',
|
||||
# 'WARNING_CONTENT':'',
|
||||
# 'WARNING_DATE':''
|
||||
# }
|
||||
# }
|
||||
|
||||
# query_data_list_item_nos_data = {
|
||||
# "funcModule": "数据项",
|
||||
# "funcOperation": "查询",
|
||||
# "data": {
|
||||
# "dateStart":"20200101",
|
||||
# "dateEnd":"20241231",
|
||||
# "dataItemNoList":["Brentzdj","Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
# }
|
||||
# }
|
||||
|
||||
|
||||
# push_data_value_list_data = {
|
||||
# "funcModule": "数据表信息列表",
|
||||
# "funcOperation": "新增",
|
||||
# "data": [
|
||||
# {"dataItemNo": "91230600716676129",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.11
|
||||
# },
|
||||
# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.55
|
||||
# },
|
||||
# {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
# "dataDate": "20230113",
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 100.55
|
||||
# }
|
||||
# ]
|
||||
# }
|
||||
# # 八大维度数据项编码
|
||||
# bdwd_items = {
|
||||
# 'ciri': '原油大数据预测|FORECAST|PRICE|T',
|
||||
# 'benzhou': '原油大数据预测|FORECAST|PRICE|W',
|
||||
# 'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
|
||||
# 'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
|
||||
# 'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
|
||||
# 'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
|
||||
# 'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
|
||||
# 'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
|
||||
# }
|
||||
|
||||
|
||||
# # 生产环境数据库
|
||||
# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
|
||||
# port = 3306
|
||||
# dbusername ='jingbo'
|
||||
# password = 'shihua@123'
|
||||
# dbname = 'jingbo'
|
||||
# table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
# # 变量定义--测试环境
|
||||
server_host = '192.168.100.53:8080' # 内网
|
||||
# server_host = '183.242.74.28' # 外网
|
||||
login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
|
||||
upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
|
||||
upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
|
||||
query_data_list_item_nos_url = f"http://{server_host}/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# 上传数据项值
|
||||
push_data_value_list_url = f"http://{server_host}/jingbo-dev/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_test",
|
||||
# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
upload_data = {
|
||||
"groupNo": '', # 用户组id
|
||||
"funcModule": '研究报告信息',
|
||||
"funcOperation": '上传石油焦普货价格预测报告',
|
||||
"data": {
|
||||
"ownerAccount": 'arui', # 报告所属用户账号
|
||||
"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
"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
|
||||
"fileBase64": '', # 文件内容base64
|
||||
"categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
"smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
"reportEmployeeCode": "E40116", # 报告人
|
||||
"reportDeptCode": "D0044", # 报告部门
|
||||
"productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
warning_data = {
|
||||
"groupNo": '', # 用户组id
|
||||
"funcModule": '石油焦普货特征停更预警',
|
||||
"funcOperation": '石油焦普货特征停更预警',
|
||||
"data": {
|
||||
'WARNING_TYPE_NAME': '特征数据停更预警',
|
||||
'WARNING_CONTENT': '',
|
||||
'WARNING_DATE': ''
|
||||
}
|
||||
}
|
||||
|
||||
query_data_list_item_nos_data = {
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询",
|
||||
"data": {
|
||||
"dateStart": "20200101",
|
||||
"dateEnd": "20241231",
|
||||
"dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
}
|
||||
}
|
||||
|
||||
push_data_value_list_data = {
|
||||
"funcModule": "数据表信息列表",
|
||||
"funcOperation": "新增",
|
||||
"data": [
|
||||
{"dataItemNo": "91230600716676129",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.11
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
# 八大维度数据项编码
|
||||
bdwd_items = {
|
||||
'ciri': 'syjlyycbdwdcr',
|
||||
'benzhou': 'syjlyycbdwdbz',
|
||||
'cizhou': 'syj|nextweek|price',
|
||||
'gezhou': 'syj|next-one-week|price',
|
||||
'ciyue': 'syj|next-month|price',
|
||||
'cieryue': 'syj|next-one-month|price',
|
||||
'cisanyue': 'yj|next-two-month|price',
|
||||
'cisiyue': 'yj|next-three-month|price',
|
||||
}
|
||||
|
||||
# 报告中八大维度数据项重命名
|
||||
columnsrename = {'syjlyycbdwdcr': '次日', 'syjlyycbdwdbz': '本周',
|
||||
'syj|nextweek|price': '次周', 'syj|next-one-week|price': '隔周',
|
||||
'syj|next-month|price': '次月', 'syj|next-one-month|price': '次二月', 'yj|next-two-month|price': '次三月', 'yj|next-three-month|price': '次四月'}
|
||||
# 北京环境数据库
|
||||
host = '192.168.101.27'
|
||||
port = 3306
|
||||
dbusername = 'root'
|
||||
password = '123456'
|
||||
dbname = 'jingbo_test'
|
||||
|
||||
# 京博测试环境
|
||||
# host = 'rm-2zehj3r1n60ttz9x5ko.mysql.rds.aliyuncs.com'
|
||||
# port = 3306
|
||||
# dbusername = 'jingbo'
|
||||
# password = 'shihua@123'
|
||||
# dbname = 'jingbo-test'
|
||||
|
||||
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
baichuan_table_name = 'V_TBL_BAICHUAN_YINGFU_VALUE'
|
||||
# select BAICHUAN_ID, DATA_DATE, DATA_VALUE from V_TBL_BAICHUAN_YINGFU_VALUE where BAICHUAN_ID in ('1588348470396475286', '1666')
|
||||
# 开关
|
||||
is_train = True # 是否训练
|
||||
is_debug = False # 是否调试
|
||||
is_eta = True # 是否使用eta接口
|
||||
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
|
||||
is_timefurture = True # 是否使用时间特征
|
||||
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
|
||||
is_edbcode = False # 特征使用edbcoding列表中的
|
||||
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
|
||||
is_update_eta = True # 预测结果上传到eta
|
||||
is_update_report = True # 是否上传报告
|
||||
is_update_warning_data = False # 是否上传预警数据
|
||||
is_update_predict_value = True # 是否上传预测值到市场信息平台
|
||||
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
|
||||
is_del_tow_month = False # 是否删除两个月不更新的特征
|
||||
is_bdwd = True # 是否使用八大维度
|
||||
|
||||
|
||||
# 连接到数据库
|
||||
db_mysql = MySQLDB(host=host, user=dbusername,
|
||||
password=password, database=dbname)
|
||||
db_mysql.connect()
|
||||
print("数据库连接成功", host, dbname, dbusername)
|
||||
|
||||
|
||||
# 数据截取日期
|
||||
start_year = 2000 # 数据开始年份
|
||||
end_time = '' # 数据截取日期
|
||||
freq = 'M' # 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 "WW" 自定义周
|
||||
delweekenday = True if freq == 'B' else False # 是否删除周末数据
|
||||
is_corr = False # 特征是否参与滞后领先提升相关系数
|
||||
add_kdj = False # 是否添加kdj指标
|
||||
if add_kdj and is_edbnamelist:
|
||||
edbnamelist = edbnamelist+['K', 'D', 'J']
|
||||
|
||||
# 模型参数
|
||||
y = '石油焦：3 # B：市场低端价：山东（日）'
|
||||
avg_cols = [
|
||||
|
||||
]
|
||||
offsite = 80
|
||||
offsite_col = []
|
||||
horizon = 4 # 预测的步长
|
||||
input_size = 8 # 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 = 100 # 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 = '石油焦普货指标数据.xlsx' # 数据集文件
|
||||
dataset = 'shiyoujiaopuhuoyuedudataset' # 数据集文件夹
|
||||
|
||||
# 数据库名称
|
||||
db_name = os.path.join(dataset, 'jbsh_shiyoujiao_puhuo_yuedu.db')
|
||||
sqlitedb = SQLiteHandler(db_name)
|
||||
sqlitedb.connect()
|
||||
|
||||
settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}'
|
||||
# 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'石油焦普货大模型月度预测报告--{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)
|
||||
470
config_shiyoujiao_puhuo_zhoudu.py
Normal file
470
config_shiyoujiao_puhuo_zhoudu.py
Normal file
@ -0,0 +1,470 @@
|
||||
import logging
|
||||
import os
|
||||
import logging.handlers
|
||||
import datetime
|
||||
from lib.tools import MySQLDB, SQLiteHandler
|
||||
|
||||
|
||||
# eta 接口token
|
||||
APPID = "XNLDvxZHHugj7wJ7"
|
||||
SECRET = "iSeU4s6cKKBVbt94htVY1p0sqUMqb2xa"
|
||||
|
||||
# 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='
|
||||
uniquecodedataurl = 'http://10.189.2.78:8108/v1/edb/data?UniqueCode=4991c37becba464609b409909fe4d992&StartDate=2024-02-01'
|
||||
classifyidlisturl = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId='
|
||||
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'
|
||||
|
||||
edbcodenamedict = {
|
||||
'C2403283369': '预赔阳极加工利润（高端）',
|
||||
'C2403285560': '预培阳极加工利润（低端）',
|
||||
'C2403288616': '低硫石油焦煅烧利润',
|
||||
'S6949656': '平均价：氧化铝：一级：全国',
|
||||
'S5807052': '氧化铝：一级：贵阳',
|
||||
'S5443355': '市场价：煤沥青：河北地区',
|
||||
'S5443357': '市场价：煤沥青：山西地区',
|
||||
'W000294': '国内主要港口石油焦出货量（隆重）',
|
||||
'W000293': '日照港库存（隆重）',
|
||||
'W000292': '港口总库存（隆重）',
|
||||
'W000283': '主营石油焦产量（隆重）',
|
||||
'W000282': '地炼石油焦产量（隆重）',
|
||||
'W000281': '中国石油焦产量（隆重）',
|
||||
'W000280': '主营石油焦开工负荷率（隆重）',
|
||||
'W000279': '地炼石油焦开工负荷率（隆重）',
|
||||
'ID00150273': '石油焦：1 # ：市场低端价：东北地区（日）',
|
||||
'ID00150281': '石油焦：1 # ：市场主流价：东北地区（日）',
|
||||
'ID00150277': '石油焦：1 # ：市场高端价：东北地区（日）',
|
||||
'ID00150289': '石油焦：2 # ：市场低端价：华东地区（日）',
|
||||
'ID00150285': '石油焦：2 # ：市场低端价：西北地区（日）',
|
||||
'ID00150313': '石油焦：2 # ：市场主流价：华东地区（日）',
|
||||
'ID00150309': '石油焦：2 # ：市场主流价：西北地区（日）',
|
||||
'ID00150301': '石油焦：2 # ：市场高端价：华东地区（日）',
|
||||
'ID00150297': '石油焦：2 # ：市场高端价：西北地区（日）',
|
||||
'ID00150321': '石油焦：2 # A：市场低端价：山东（日）',
|
||||
'ID00150329': '石油焦：2 # A：市场主流价：山东（日）',
|
||||
'ID00150325': '石油焦：2 # A：市场高端价：山东（日）',
|
||||
'ID00150337': '石油焦：2 # B：市场低端价：华南地区（日）',
|
||||
'ID00150341': '石油焦：2 # B：市场低端价：华中地区（日）',
|
||||
'ID00150361': '石油焦：2 # B：市场主流价：华南地区（日）',
|
||||
'ID00150365': '石油焦：2 # B：市场主流价：华中地区（日）',
|
||||
'ID00150349': '石油焦：2 # B：市场高端价：华南地区（日）',
|
||||
'ID00150353': '石油焦：2 # B：市场高端价：华中地区（日）',
|
||||
'ID00150333': '石油焦：2 # B：市场低端价：山东（日）',
|
||||
'ID00150357': '石油焦：2 # B：市场主流价：山东（日）',
|
||||
'ID00150345': '石油焦：2 # B：市场高端价：山东（日）',
|
||||
'ID00150369': '石油焦：3 # ：市场低端价：华中地区（日）',
|
||||
'ID00150373': '石油焦：3 # ：市场高端价：华中地区（日）',
|
||||
'ID00150385': '石油焦：3 # A：市场高端价：山东（日）',
|
||||
'ID00150393': '石油焦：3 # B：市场低端价：华东地区（日）',
|
||||
'ID00150409': '石油焦：3 # B：市场主流价：华东地区（日）',
|
||||
'ID00150401': '石油焦：3 # B：市场高端价：华东地区（日）',
|
||||
'ID00146589': '海绵焦：4 # ：出厂价：华中地区：洛阳石化（日）',
|
||||
'ID01242846': '石油焦：4 # B：挂牌价：华北地区：中石化燕山（日）',
|
||||
'ID01300358': '石油焦：3 # C：市场低端价：山东（日）',
|
||||
'ID01300357': '石油焦：3 # C：市场高端价：山东（日）',
|
||||
'ID00150377': '石油焦：3 # ：市场主流价：华中地区（日）',
|
||||
'ID01387643': '煅烧焦：低硫：0.5 % S：市场价：东北地区（日）',
|
||||
'ID01387646': '煅烧焦：低硫：3.5 % S：市场价：东北地区（日）',
|
||||
'ID01387660': '煅烧焦：中硫：3 % S，400V：市场价：山东（日）',
|
||||
'ID00150381': '石油焦：3 # A：市场低端价：山东（日）',
|
||||
'ID00150397': '石油焦：3 # B：市场低端价：山东（日）',
|
||||
'ID00150405': '石油焦：3 # B：市场高端价：山东（日）',
|
||||
'ID00146545': '海绵焦：3B：出厂价：山东：山东东明（日）',
|
||||
'B3e90b34e4b9e7a6ea3': '石油焦市场均价(元/吨)',
|
||||
'B6b5c53b270a3af12ac': '石油焦1 # 市场均价(元/吨)',
|
||||
'B10721189a11c209a20': '石油焦2 # 市场均价(元/吨)',
|
||||
'B6accfa9d2bf4735a50': '石油焦3 # 市场均价(元/吨)',
|
||||
'B8a0ab5357569c385a9': '石油焦海绵焦市场均价(元/吨)',
|
||||
'B19dcf45e22fbfd3e43': '石油焦海绵焦东北1 # A焦（低端）(元/吨)（百川）',
|
||||
'B5832a62d1e0fba50b6': '石油焦海绵焦东北1 # A焦（高端）(元/吨)（百川）',
|
||||
'B1de4fba026d4609cc7': '石油焦海绵焦东北1 # B焦（低端）(元/吨)（百川）',
|
||||
'B38f89180736172490d': '石油焦海绵焦东北1 # B焦（高端）(元/吨)（百川）',
|
||||
'B4f847871674c3d77f2': '石油焦海绵焦山东地炼1 # -3#焦（低端）(元/吨)',
|
||||
'B1aefb8a64a5200adbd': '石油焦海绵焦山东地炼1 # -3#焦（高端）(元/吨)',
|
||||
'B1df7d0afbfedfb628a': '煅烧焦东北低硫(高端S < 0.5)(元/吨)',
|
||||
'B5f8f9859635876da28': '煅烧焦东北低硫(低端S < 0.5)(元/吨)',
|
||||
'B2342a8c5a39fa00348': '煅烧焦华北中硫(高端S < 3.0，钒 < 400)(元/吨)',
|
||||
'B051f27900397c6a35f': '煅烧焦山东中硫(高端S < 3.0，钒 < 400)(元/吨)',
|
||||
'Be2a8050a48e86cae1f': '煅烧焦华东中硫(高端S < 3.0，钒 < 400)(元/吨)',
|
||||
'B4a1811938f85065f6a': '煅烧焦华中中硫(高端S < 3.0，钒 < 400)(元/吨)',
|
||||
'Bc197d4834ef7fb98ec': '煅烧焦华东高硫(高端S < 3.5，钒 < 400)(元/吨)',
|
||||
'B62be5dbdb8c6454530': '煅烧焦低硫参考价格(元/吨)（百川）',
|
||||
'Bdd813140bffc4edfa6': '煅烧焦中硫微量市场均价(元/吨)（百川）',
|
||||
'B185a597decfc71915a': '预焙阳极山东低端(元/吨)（百川）',
|
||||
'B1bcde6130de031bd42': '山西 改质沥青(元/吨)',
|
||||
'Bb9f4a1f6dd32b4ad8a': '山东 改质沥青(元/吨)',
|
||||
'C2411261557491549': '石油焦市场均价(元/吨)/4DMA',
|
||||
'C2411271143174617': '石油焦市场均价(元/吨)/9DMA',
|
||||
'ID01387649': '煅烧焦：中硫：3 % S，350V：市场价：华东地区（日）',
|
||||
'ID01387655': '煅烧焦：中硫：3 % S，350V：市场价：山东（日）',
|
||||
'RE00010076': '煅烧焦：低硫：生产毛利：东北地区（周）',
|
||||
'B9d1acaf80383683da3': '石油焦总产量（周）(吨)',
|
||||
'Bdaa719a38936c8dd76': '石油焦开工率（周）( % )',
|
||||
'B9459d549a332b200e7': '石油焦行业总库存（周）(吨)',
|
||||
'Bce6e098b9518370cff': '石油焦工厂库存（周）(吨)',
|
||||
'B577ce2809772779710': '石油焦市场库存（周）(吨)',
|
||||
'B5d8c564c62f3e6b77f': '石油焦成本（周）(吨)',
|
||||
'B43baa98bcaa06c11a5': '石油焦利润（周）(吨)',
|
||||
'Bdd0c1361d94081211c': '煅烧石油焦总产量（周）(吨)',
|
||||
'B65315111fa28951b1e': '煅烧石油焦开工率（周）( % )',
|
||||
'B2aff5f2632a20027d0': '煅烧石油焦行业总库存（周）(吨)',
|
||||
'B29fbd31128cd71b212': '煅烧石油焦工厂库存（周）(吨)',
|
||||
'B7a88313a89d1261c53': '煅烧石油焦成本（周）(吨)',
|
||||
'Bd4fa36b4decec0aafa': '煅烧石油焦利润（周）(吨)',
|
||||
'B9bd80eac7df81ffbd4': '预焙阳极总产量（周）(吨)',
|
||||
'B27074786605f4660d2': '预焙阳极开工率（周）( % )',
|
||||
'Bdc2a5985ecb56b6a0c': '预焙阳极行业总库存（周）(吨)',
|
||||
'Bce8511f899e487e5b6': '预焙阳极工厂库存（周）(吨)',
|
||||
'B13ec89105bd866a2bd': '预焙阳极成本（周）(吨)',
|
||||
'B66c3abcfa15a2e611c': '预焙阳极利润（周）(吨)',
|
||||
'Bf7efe3200f9abc0453': '电解铝开工率（周）( % )',
|
||||
'Be193166f347267b1a7': '电解铝行业总库存（周）(吨)',
|
||||
'Baa744fc97769353175': '电解铝工厂库存（周）(吨)',
|
||||
'Bf9654603913cfc5282': '电解铝市场库存（周）(吨)',
|
||||
'Bef1535c96da0d70fbc': '电解铝利润（周）(吨)',
|
||||
'B7d1d0b24316d49cbdc': '煤沥青总产量（周）(吨)',
|
||||
'B4303fb002ea1c214da': '煤沥青开工率（周）( % )',
|
||||
'Be9a470c97e9efe660c': '煤沥青行业总库存（周）(吨)',
|
||||
'B50d4d87f6b78bca587': '煤沥青工厂库存（周）(吨)',
|
||||
'B46cc7d0a90155b5bfd': '煅烧焦山东高硫(高端S < 3.5，普货)(元/吨)'
|
||||
}
|
||||
|
||||
edbcodelist = edbcodenamedict.keys()
|
||||
# 临时写死用指定的列,与上面的edbcode对应，后面更改
|
||||
edbnamelist = ['ds', 'y']+[edbcodenamedict[edbcodename]
|
||||
for edbcodename in edbcodelist]
|
||||
|
||||
# eta自有数据指标编码，石油焦铝用还没新增，暂且留空
|
||||
# eta自有数据指标编码 次周,隔周
|
||||
bdwdname = [
|
||||
'次周',
|
||||
'隔周',
|
||||
]
|
||||
modelsindex = [
|
||||
{"NHITS":"SELF0000407",
|
||||
"Informer":"SELF0000408",
|
||||
"LSTM":"SELF0000409",
|
||||
"iTransformer":"SELF0000410",
|
||||
"TSMixer":"SELF0000411",
|
||||
"TSMixerx":"SELF0000412",
|
||||
"PatchTST":"SELF0000413",
|
||||
"RNN":"SELF0000414",
|
||||
"GRU":"SELF0000415",
|
||||
"TCN":"SELF0000416",
|
||||
"BiTCN":"SELF0000417",
|
||||
"DilatedRNN":"SELF0000418",
|
||||
"MLP":"SELF0000419",
|
||||
"DLinear":"SELF0000420",
|
||||
"NLinear":"SELF0000421",
|
||||
"TFT":"SELF0000422",
|
||||
"FEDformer":"SELF0000423",
|
||||
"StemGNN":"SELF0000424",
|
||||
"MLPMultivariate":"SELF0000425",
|
||||
"TiDE":"SELF0000426",
|
||||
"DeepNPTS":"SELF0000427",
|
||||
"NBEATS":"SELF0000428",
|
||||
|
||||
},{"NHITS":"SELF0000429",
|
||||
"Informer":"SELF0000430",
|
||||
"LSTM":"SELF0000431",
|
||||
"iTransformer":"SELF0000432",
|
||||
"TSMixer":"SELF0000433",
|
||||
"TSMixerx":"SELF0000434",
|
||||
"PatchTST":"SELF0000435",
|
||||
"RNN":"SELF0000436",
|
||||
"GRU":"SELF0000437",
|
||||
"TCN":"SELF0000438",
|
||||
"BiTCN":"SELF0000439",
|
||||
"DilatedRNN":"SELF0000440",
|
||||
"MLP":"SELF0000441",
|
||||
"DLinear":"SELF0000442",
|
||||
"NLinear":"SELF0000443",
|
||||
"TFT":"SELF0000444",
|
||||
"FEDformer":"SELF0000445",
|
||||
"StemGNN":"SELF0000446",
|
||||
"MLPMultivariate":"SELF0000447",
|
||||
"TiDE":"SELF0000448",
|
||||
"DeepNPTS":"SELF0000449",
|
||||
"NBEATS":"SELF0000450",}
|
||||
]
|
||||
|
||||
# 百川数据指标编码
|
||||
baichuanidnamedict = {
|
||||
'1588348470396480901': '石油焦滨州-友泰',
|
||||
'1588348470396480903': '石油焦东营-海科瑞林',
|
||||
'1588348470396480902': '石油焦东营-华联2',
|
||||
'1588348470396481080': '石油焦东营-华联3',
|
||||
'1588348470396480905': '石油焦东营-联合',
|
||||
'1588348470396481081': '石油焦东营-联合3',
|
||||
'1588348470396480915': '石油焦淄博-汇丰',
|
||||
'1588348470396480888': '石油焦沧州-鑫海',
|
||||
'1588348470396480917': '石油焦东营-万通',
|
||||
'1588348470396480925': '石油焦东营-齐润',
|
||||
'1588348470396481084': '石油焦东营-尚能4',
|
||||
'1588348470396480930': '石油焦潍坊-寿光鲁清',
|
||||
'1588348470396480929': '石油焦滨州-鑫岳'
|
||||
}
|
||||
|
||||
|
||||
# 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": "",
|
||||
"IndexName": "石油焦铝用价格预测xx模型-yy",
|
||||
"Unit": "无",
|
||||
"Frequency": "日度",
|
||||
"SourceName": f"价格预测",
|
||||
"Remark": 'ddd',
|
||||
"DataList": [
|
||||
{
|
||||
"Date": "2024-05-02",
|
||||
"Value": 333444
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
# eta 分类
|
||||
# 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
|
||||
# url = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId=1214'
|
||||
# ParentId ":1160, 能源化工
|
||||
# ClassifyId ":1214,原油 3912 石油焦
|
||||
# ParentId ":1214,"，就是原油下所有的数据。
|
||||
ClassifyId = 3707
|
||||
|
||||
|
||||
# 变量定义--测试环境
|
||||
server_host = '192.168.100.53' # 内网
|
||||
# server_host = '183.242.74.28' # 外网
|
||||
login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
|
||||
# 上传报告
|
||||
upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
|
||||
# 停更预警
|
||||
upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
|
||||
# 查询数据项编码
|
||||
query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# 上传数据项值
|
||||
push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_test",
|
||||
# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
upload_data = {
|
||||
"groupNo": '', # 用户组id
|
||||
"funcModule": '研究报告信息',
|
||||
"funcOperation": '上传原油价格预测报告',
|
||||
"data": {
|
||||
"ownerAccount": 'arui', # 报告所属用户账号
|
||||
"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
"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
|
||||
"fileBase64": '', # 文件内容base64
|
||||
"categoryNo": 'yyjgycbg', # 研究报告分类编码
|
||||
"smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
|
||||
"reportEmployeeCode": "E40116", # 报告人
|
||||
"reportDeptCode": "D0044", # 报告部门
|
||||
"productGroupCode": "RAW_MATERIAL" # 商品分类
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
warning_data = {
|
||||
"groupNo": '', # 用户组id
|
||||
"funcModule": '原油特征停更预警',
|
||||
"funcOperation": '原油特征停更预警',
|
||||
"data": {
|
||||
'WARNING_TYPE_NAME': '特征数据停更预警',
|
||||
'WARNING_CONTENT': '',
|
||||
'WARNING_DATE': ''
|
||||
}
|
||||
}
|
||||
|
||||
query_data_list_item_nos_data = {
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询",
|
||||
"data": {
|
||||
"dateStart": "20200101",
|
||||
"dateEnd": "20241231",
|
||||
"dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||
}
|
||||
}
|
||||
|
||||
push_data_value_list_data = {
|
||||
"funcModule": "数据表信息列表",
|
||||
"funcOperation": "新增",
|
||||
"data": [
|
||||
{"dataItemNo": "91230600716676129",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.11
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
},
|
||||
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
|
||||
"dataDate": "20230113",
|
||||
"dataStatus": "add",
|
||||
"dataValue": 100.55
|
||||
}
|
||||
]
|
||||
}
|
||||
# 八大维度数据项编码
|
||||
bdwd_items = {
|
||||
'ciri': 'syjlyycbdwdcr',
|
||||
'benzhou': 'syjlyycbdwdbz',
|
||||
'cizhou': 'syj|nextweek|price',
|
||||
'gezhou': 'syj|next-one-week|price',
|
||||
'ciyue': 'syj|next-month|price',
|
||||
'cieryue': 'syj|next-one-month|price',
|
||||
'cisanyue': 'yj|next-two-month|price',
|
||||
'cisiyue': 'yj|next-three-month|price',
|
||||
}
|
||||
|
||||
# 北京环境数据库
|
||||
# host = '192.168.101.27'
|
||||
# port = 3306
|
||||
# dbusername = 'root'
|
||||
# password = '123456'
|
||||
# dbname = 'jingbo_test'
|
||||
|
||||
# 京博测试环境
|
||||
host = 'rm-2zehj3r1n60ttz9x5ko.mysql.rds.aliyuncs.com'
|
||||
port = 3306
|
||||
dbusername = 'jingbo'
|
||||
password = 'shihua@123'
|
||||
dbname = 'jingbo-test'
|
||||
|
||||
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
baichuan_table_name = 'V_TBL_BAICHUAN_YINGFU_VALUE'
|
||||
# select BAICHUAN_ID, DATA_DATE, DATA_VALUE from V_TBL_BAICHUAN_YINGFU_VALUE where BAICHUAN_ID in ('1588348470396475286', '1666')
|
||||
# 开关
|
||||
is_train = True # 是否训练
|
||||
is_debug = False # 是否调试
|
||||
is_eta = True # 是否使用eta接口
|
||||
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
|
||||
is_timefurture = True # 是否使用时间特征
|
||||
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
|
||||
is_edbcode = False # 特征使用edbcoding列表中的
|
||||
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
|
||||
is_update_eta = True # 预测结果上传到eta
|
||||
is_update_report = True # 是否上传报告
|
||||
is_update_warning_data = False # 是否上传预警数据
|
||||
is_update_predict_value = True # 是否上传预测值到市场信息平台
|
||||
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
|
||||
is_del_tow_month = False # 是否删除两个月不更新的特征
|
||||
is_bdwd = False # 是否使用八大维度
|
||||
|
||||
|
||||
# 连接到数据库
|
||||
db_mysql = MySQLDB(host=host, user=dbusername,
|
||||
password=password, database=dbname)
|
||||
db_mysql.connect()
|
||||
print("数据库连接成功", host, dbname, dbusername)
|
||||
|
||||
|
||||
# 数据截取日期
|
||||
start_year = 2015 # 数据开始年份
|
||||
end_time = '' # 数据截取日期
|
||||
freq = 'WW' # 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 "WW" 自定义周
|
||||
delweekenday = True if freq == 'B' else False # 是否删除周末数据
|
||||
is_corr = False # 特征是否参与滞后领先提升相关系数
|
||||
add_kdj = False # 是否添加kdj指标
|
||||
if add_kdj and is_edbnamelist:
|
||||
edbnamelist = edbnamelist+['K', 'D', 'J']
|
||||
|
||||
# 模型参数
|
||||
y = '石油焦：3 # B：市场低端价：山东（日）'
|
||||
avg_cols = [
|
||||
|
||||
]
|
||||
offsite = 80
|
||||
offsite_col = []
|
||||
horizon = 2 # 预测的步长
|
||||
input_size = 14 # 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 = '石油焦铝用指标数据.xlsx' # 数据集文件
|
||||
dataset = 'shiyoujiaolvyongzhoududataset' # 数据集文件夹
|
||||
|
||||
# 数据库名称
|
||||
db_name = os.path.join(dataset, 'jbsh_shiyoujiao_lvyong_zhoudu.db')
|
||||
sqlitedb = SQLiteHandler(db_name)
|
||||
sqlitedb.connect()
|
||||
|
||||
settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}'
|
||||
# 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'石油焦铝用大模型周度预测报告--{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)
|
||||
353
eta数据调试.py
353
eta数据调试.py
@ -128,182 +128,183 @@ modelsindex = {
|
||||
}
|
||||
|
||||
selfid = {
|
||||
"石油焦-铝用价格预测NHITS模型-次日": "SELF0000363",
|
||||
"石油焦-铝用价格预测Informer模型-次日": "SELF0000364",
|
||||
"石油焦-铝用价格预测LSTM模型-次日": "SELF0000365",
|
||||
"石油焦-铝用价格预测iTransformer模型-次日": "SELF0000366",
|
||||
"石油焦-铝用价格预测TSMixer模型-次日": "SELF0000367",
|
||||
"石油焦-铝用价格预测TSMixerx模型-次日": "SELF0000368",
|
||||
"石油焦-铝用价格预测PatchTST模型-次日": "SELF0000369",
|
||||
"石油焦-铝用价格预测RNN模型-次日": "SELF0000370",
|
||||
"石油焦-铝用价格预测GRU模型-次日": "SELF0000371",
|
||||
"石油焦-铝用价格预测TCN模型-次日": "SELF0000372",
|
||||
"石油焦-铝用价格预测BiTCN模型-次日": "SELF0000373",
|
||||
"石油焦-铝用价格预测DilatedRNN模型-次日": "SELF0000374",
|
||||
"石油焦-铝用价格预测MLP模型-次日": "SELF0000375",
|
||||
"石油焦-铝用价格预测DLinear模型-次日": "SELF0000376",
|
||||
"石油焦-铝用价格预测NLinear模型-次日": "SELF0000377",
|
||||
"石油焦-铝用价格预测TFT模型-次日": "SELF0000378",
|
||||
"石油焦-铝用价格预测FEDformer模型-次日": "SELF0000379",
|
||||
"石油焦-铝用价格预测StemGNN模型-次日": "SELF0000380",
|
||||
"石油焦-铝用价格预测MLPMultivariate模型-次日": "SELF0000381",
|
||||
"石油焦-铝用价格预测TiDE模型-次日": "SELF0000382",
|
||||
"石油焦-铝用价格预测DeepNPTS模型-次日": "SELF0000383",
|
||||
"石油焦-铝用价格预测NBEATS模型-次日": "SELF0000384",
|
||||
"石油焦-铝用价格预测NHITS模型-本周": "SELF0000385",
|
||||
"石油焦-铝用价格预测Informer模型-本周": "SELF0000386",
|
||||
"石油焦-铝用价格预测LSTM模型-本周": "SELF0000387",
|
||||
"石油焦-铝用价格预测iTransformer模型-本周": "SELF0000388",
|
||||
"石油焦-铝用价格预测TSMixer模型-本周": "SELF0000389",
|
||||
"石油焦-铝用价格预测TSMixerx模型-本周": "SELF0000390",
|
||||
"石油焦-铝用价格预测PatchTST模型-本周": "SELF0000391",
|
||||
"石油焦-铝用价格预测RNN模型-本周": "SELF0000392",
|
||||
"石油焦-铝用价格预测GRU模型-本周": "SELF0000393",
|
||||
"石油焦-铝用价格预测TCN模型-本周": "SELF0000394",
|
||||
"石油焦-铝用价格预测BiTCN模型-本周": "SELF0000395",
|
||||
"石油焦-铝用价格预测DilatedRNN模型-本周": "SELF0000396",
|
||||
"石油焦-铝用价格预测MLP模型-本周": "SELF0000397",
|
||||
"石油焦-铝用价格预测DLinear模型-本周": "SELF0000398",
|
||||
"石油焦-铝用价格预测NLinear模型-本周": "SELF0000399",
|
||||
"石油焦-铝用价格预测TFT模型-本周": "SELF0000400",
|
||||
"石油焦-铝用价格预测FEDformer模型-本周": "SELF0000401",
|
||||
"石油焦-铝用价格预测StemGNN模型-本周": "SELF0000402",
|
||||
"石油焦-铝用价格预测MLPMultivariate模型-本周": "SELF0000403",
|
||||
"石油焦-铝用价格预测TiDE模型-本周": "SELF0000404",
|
||||
"石油焦-铝用价格预测DeepNPTS模型-本周": "SELF0000405",
|
||||
"石油焦-铝用价格预测NBEATS模型-本周": "SELF0000406",
|
||||
"石油焦-铝用价格预测NHITS模型-次周": "SELF0000407",
|
||||
"石油焦-铝用价格预测Informer模型-次周": "SELF0000408",
|
||||
"石油焦-铝用价格预测LSTM模型-次周": "SELF0000409",
|
||||
"石油焦-铝用价格预测iTransformer模型-次周": "SELF0000410",
|
||||
"石油焦-铝用价格预测TSMixer模型-次周": "SELF0000411",
|
||||
"石油焦-铝用价格预测TSMixerx模型-次周": "SELF0000412",
|
||||
"石油焦-铝用价格预测PatchTST模型-次周": "SELF0000413",
|
||||
"石油焦-铝用价格预测RNN模型-次周": "SELF0000414",
|
||||
"石油焦-铝用价格预测GRU模型-次周": "SELF0000415",
|
||||
"石油焦-铝用价格预测TCN模型-次周": "SELF0000416",
|
||||
"石油焦-铝用价格预测BiTCN模型-次周": "SELF0000417",
|
||||
"石油焦-铝用价格预测DilatedRNN模型-次周": "SELF0000418",
|
||||
"石油焦-铝用价格预测MLP模型-次周": "SELF0000419",
|
||||
"石油焦-铝用价格预测DLinear模型-次周": "SELF0000420",
|
||||
"石油焦-铝用价格预测NLinear模型-次周": "SELF0000421",
|
||||
"石油焦-铝用价格预测TFT模型-次周": "SELF0000422",
|
||||
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|
||||
"石油焦-普货价格预测TSMixer模型-隔周": "SELF0000609",
|
||||
"石油焦-普货价格预测TSMixerx模型-隔周": "SELF0000610",
|
||||
"石油焦-普货价格预测PatchTST模型-隔周": "SELF0000611",
|
||||
"石油焦-普货价格预测RNN模型-隔周": "SELF0000612",
|
||||
"石油焦-普货价格预测GRU模型-隔周": "SELF0000613",
|
||||
"石油焦-普货价格预测TCN模型-隔周": "SELF0000614",
|
||||
"石油焦-普货价格预测BiTCN模型-隔周": "SELF0000615",
|
||||
"石油焦-普货价格预测DilatedRNN模型-隔周": "SELF0000616",
|
||||
"石油焦-普货价格预测MLP模型-隔周": "SELF0000617",
|
||||
"石油焦-普货价格预测DLinear模型-隔周": "SELF0000618",
|
||||
"石油焦-普货价格预测NLinear模型-隔周": "SELF0000619",
|
||||
"石油焦-普货价格预测TFT模型-隔周": "SELF0000620",
|
||||
"石油焦-普货价格预测FEDformer模型-隔周": "SELF0000621",
|
||||
"石油焦-普货价格预测StemGNN模型-隔周": "SELF0000622",
|
||||
"石油焦-普货价格预测MLPMultivariate模型-隔周": "SELF0000623",
|
||||
"石油焦-普货价格预测TiDE模型-隔周": "SELF0000624",
|
||||
"石油焦-普货价格预测DeepNPTS模型-隔周": "SELF0000625",
|
||||
"石油焦-普货价格预测NBEATS模型-隔周": "SELF0000626",
|
||||
"石油焦-普货价格预测NHITS模型-次月": "SELF0000627",
|
||||
"石油焦-普货价格预测Informer模型-次月": "SELF0000628",
|
||||
"石油焦-普货价格预测LSTM模型-次月": "SELF0000629",
|
||||
"石油焦-普货价格预测iTransformer模型-次月": "SELF0000630",
|
||||
"石油焦-普货价格预测TSMixer模型-次月": "SELF0000631",
|
||||
"石油焦-普货价格预测TSMixerx模型-次月": "SELF0000632",
|
||||
"石油焦-普货价格预测PatchTST模型-次月": "SELF0000633",
|
||||
"石油焦-普货价格预测RNN模型-次月": "SELF0000634",
|
||||
"石油焦-普货价格预测GRU模型-次月": "SELF0000635",
|
||||
"石油焦-普货价格预测TCN模型-次月": "SELF0000636",
|
||||
"石油焦-普货价格预测BiTCN模型-次月": "SELF0000637",
|
||||
"石油焦-普货价格预测DilatedRNN模型-次月": "SELF0000638",
|
||||
"石油焦-普货价格预测MLP模型-次月": "SELF0000639",
|
||||
"石油焦-普货价格预测DLinear模型-次月": "SELF0000640",
|
||||
"石油焦-普货价格预测NLinear模型-次月": "SELF0000641",
|
||||
"石油焦-普货价格预测TFT模型-次月": "SELF0000642",
|
||||
"石油焦-普货价格预测FEDformer模型-次月": "SELF0000643",
|
||||
"石油焦-普货价格预测StemGNN模型-次月": "SELF0000644",
|
||||
"石油焦-普货价格预测MLPMultivariate模型-次月": "SELF0000645",
|
||||
"石油焦-普货价格预测TiDE模型-次月": "SELF0000646",
|
||||
"石油焦-普货价格预测DeepNPTS模型-次月": "SELF0000647",
|
||||
"石油焦-普货价格预测NBEATS模型-次月": "SELF0000648",
|
||||
"石油焦-普货价格预测NHITS模型-次二月": "SELF0000649",
|
||||
"石油焦-普货价格预测Informer模型-次二月": "SELF0000650",
|
||||
"石油焦-普货价格预测LSTM模型-次二月": "SELF0000651",
|
||||
"石油焦-普货价格预测iTransformer模型-次二月": "SELF0000652",
|
||||
"石油焦-普货价格预测TSMixer模型-次二月": "SELF0000653",
|
||||
"石油焦-普货价格预测TSMixerx模型-次二月": "SELF0000654",
|
||||
"石油焦-普货价格预测PatchTST模型-次二月": "SELF0000655",
|
||||
"石油焦-普货价格预测RNN模型-次二月": "SELF0000656",
|
||||
"石油焦-普货价格预测GRU模型-次二月": "SELF0000657",
|
||||
"石油焦-普货价格预测TCN模型-次二月": "SELF0000658",
|
||||
"石油焦-普货价格预测BiTCN模型-次二月": "SELF0000659",
|
||||
"石油焦-普货价格预测DilatedRNN模型-次二月": "SELF0000660",
|
||||
"石油焦-普货价格预测MLP模型-次二月": "SELF0000661",
|
||||
"石油焦-普货价格预测DLinear模型-次二月": "SELF0000662",
|
||||
"石油焦-普货价格预测NLinear模型-次二月": "SELF0000663",
|
||||
"石油焦-普货价格预测TFT模型-次二月": "SELF0000664",
|
||||
"石油焦-普货价格预测FEDformer模型-次二月": "SELF0000665",
|
||||
"石油焦-普货价格预测StemGNN模型-次二月": "SELF0000666",
|
||||
"石油焦-普货价格预测MLPMultivariate模型-次二月": "SELF0000667",
|
||||
"石油焦-普货价格预测TiDE模型-次二月": "SELF0000668",
|
||||
"石油焦-普货价格预测DeepNPTS模型-次二月": "SELF0000669",
|
||||
"石油焦-普货价格预测NBEATS模型-次二月": "SELF0000670",
|
||||
"石油焦-普货价格预测NHITS模型-次三月": "SELF0000671",
|
||||
"石油焦-普货价格预测Informer模型-次三月": "SELF0000672",
|
||||
"石油焦-普货价格预测LSTM模型-次三月": "SELF0000673",
|
||||
"石油焦-普货价格预测iTransformer模型-次三月": "SELF0000674",
|
||||
"石油焦-普货价格预测TSMixer模型-次三月": "SELF0000675",
|
||||
"石油焦-普货价格预测TSMixerx模型-次三月": "SELF0000676",
|
||||
"石油焦-普货价格预测PatchTST模型-次三月": "SELF0000677",
|
||||
"石油焦-普货价格预测RNN模型-次三月": "SELF0000678",
|
||||
"石油焦-普货价格预测GRU模型-次三月": "SELF0000679",
|
||||
"石油焦-普货价格预测TCN模型-次三月": "SELF0000680",
|
||||
"石油焦-普货价格预测BiTCN模型-次三月": "SELF0000681",
|
||||
"石油焦-普货价格预测DilatedRNN模型-次三月": "SELF0000682",
|
||||
"石油焦-普货价格预测MLP模型-次三月": "SELF0000683",
|
||||
"石油焦-普货价格预测DLinear模型-次三月": "SELF0000684",
|
||||
"石油焦-普货价格预测NLinear模型-次三月": "SELF0000685",
|
||||
"石油焦-普货价格预测TFT模型-次三月": "SELF0000686",
|
||||
"石油焦-普货价格预测FEDformer模型-次三月": "SELF0000687",
|
||||
"石油焦-普货价格预测StemGNN模型-次三月": "SELF0000688",
|
||||
"石油焦-普货价格预测MLPMultivariate模型-次三月": "SELF0000689",
|
||||
"石油焦-普货价格预测TiDE模型-次三月": "SELF0000690",
|
||||
"石油焦-普货价格预测DeepNPTS模型-次三月": "SELF0000691",
|
||||
"石油焦-普货价格预测NBEATS模型-次三月": "SELF0000692",
|
||||
"石油焦-普货价格预测NHITS模型-次四月": "SELF0000693",
|
||||
"石油焦-普货价格预测Informer模型-次四月": "SELF0000694",
|
||||
"石油焦-普货价格预测LSTM模型-次四月": "SELF0000695",
|
||||
"石油焦-普货价格预测iTransformer模型-次四月": "SELF0000696",
|
||||
"石油焦-普货价格预测TSMixer模型-次四月": "SELF0000697",
|
||||
"石油焦-普货价格预测TSMixerx模型-次四月": "SELF0000698",
|
||||
"石油焦-普货价格预测PatchTST模型-次四月": "SELF0000699",
|
||||
"石油焦-普货价格预测RNN模型-次四月": "SELF0000700",
|
||||
"石油焦-普货价格预测GRU模型-次四月": "SELF0000701",
|
||||
"石油焦-普货价格预测TCN模型-次四月": "SELF0000702",
|
||||
"石油焦-普货价格预测BiTCN模型-次四月": "SELF0000703",
|
||||
"石油焦-普货价格预测DilatedRNN模型-次四月": "SELF0000704",
|
||||
"石油焦-普货价格预测MLP模型-次四月": "SELF0000705",
|
||||
"石油焦-普货价格预测DLinear模型-次四月": "SELF0000706",
|
||||
"石油焦-普货价格预测NLinear模型-次四月": "SELF0000707",
|
||||
"石油焦-普货价格预测TFT模型-次四月": "SELF0000708",
|
||||
"石油焦-普货价格预测FEDformer模型-次四月": "SELF0000709",
|
||||
"石油焦-普货价格预测StemGNN模型-次四月": "SELF0000710",
|
||||
"石油焦-普货价格预测MLPMultivariate模型-次四月": "SELF0000711",
|
||||
"石油焦-普货价格预测TiDE模型-次四月": "SELF0000712",
|
||||
"石油焦-普货价格预测DeepNPTS模型-次四月": "SELF0000713",
|
||||
"石油焦-普货价格预测NBEATS模型-次四月": "SELF0000714",
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
442
main_shiyoujiao_puhuo_yuedu.py
Normal file
442
main_shiyoujiao_puhuo_yuedu.py
Normal file
@ -0,0 +1,442 @@
|
||||
# 读取配置
|
||||
|
||||
from lib.dataread import *
|
||||
from config_shiyoujiao_puhuo_yuedu import *
|
||||
from lib.tools import SendMail, exception_logger
|
||||
from models.nerulforcastmodels import ex_Model, model_losss, shiyoujiao_puhuo_export_pdf
|
||||
import datetime
|
||||
import torch
|
||||
torch.set_float32_matmul_precision("high")
|
||||
|
||||
global_config.update({
|
||||
# 核心参数
|
||||
'logger': logger,
|
||||
'dataset': dataset,
|
||||
'y': y,
|
||||
'is_debug': is_debug,
|
||||
'is_train': is_train,
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'is_update_report': is_update_report,
|
||||
'settings': settings,
|
||||
'weight_dict': weight_dict,
|
||||
'baichuanidnamedict': baichuanidnamedict,
|
||||
'bdwdname': bdwdname,
|
||||
|
||||
|
||||
# 模型参数
|
||||
'data_set': data_set,
|
||||
'input_size': input_size,
|
||||
'horizon': horizon,
|
||||
'train_steps': train_steps,
|
||||
'val_check_steps': val_check_steps,
|
||||
'val_size': val_size,
|
||||
'test_size': test_size,
|
||||
'modelsindex': modelsindex,
|
||||
'rote': rote,
|
||||
'bdwd_items': bdwd_items,
|
||||
'baichuanidnamedict': baichuanidnamedict,
|
||||
|
||||
# 特征工程开关
|
||||
'is_del_corr': is_del_corr,
|
||||
'is_del_tow_month': is_del_tow_month,
|
||||
'is_eta': is_eta,
|
||||
'is_update_eta': is_update_eta,
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'is_update_predict_value': is_update_predict_value,
|
||||
'early_stop_patience_steps': early_stop_patience_steps,
|
||||
|
||||
# 时间参数
|
||||
'start_year': start_year,
|
||||
'end_time': end_time or datetime.datetime.now().strftime("%Y-%m-%d"),
|
||||
'freq': freq, # 保持列表结构
|
||||
|
||||
# 接口配置
|
||||
'login_pushreport_url': login_pushreport_url,
|
||||
'login_data': login_data,
|
||||
'upload_url': upload_url,
|
||||
'upload_data': upload_data,
|
||||
'upload_warning_url': upload_warning_url,
|
||||
'warning_data': warning_data,
|
||||
|
||||
# 查询接口
|
||||
'query_data_list_item_nos_url': query_data_list_item_nos_url,
|
||||
'query_data_list_item_nos_data': query_data_list_item_nos_data,
|
||||
|
||||
# 上传数据项
|
||||
'push_data_value_list_url': push_data_value_list_url,
|
||||
'push_data_value_list_data': push_data_value_list_data,
|
||||
|
||||
# eta 配置
|
||||
'APPID': APPID,
|
||||
'SECRET': SECRET,
|
||||
'etadata': data,
|
||||
'edbcodelist': edbcodelist,
|
||||
'ClassifyId': ClassifyId,
|
||||
'edbcodedataurl': edbcodedataurl,
|
||||
'classifyidlisturl': classifyidlisturl,
|
||||
'edbdatapushurl': edbdatapushurl,
|
||||
'edbdeleteurl': edbdeleteurl,
|
||||
'edbbusinessurl': edbbusinessurl,
|
||||
'edbcodenamedict': edbcodenamedict,
|
||||
'ClassifyId': ClassifyId,
|
||||
'classifylisturl': classifylisturl,
|
||||
|
||||
# 数据库配置
|
||||
'sqlitedb': sqlitedb,
|
||||
'is_bdwd': is_bdwd,
|
||||
'columnsrename': columnsrename,
|
||||
'db_mysql': db_mysql,
|
||||
'baichuan_table_name': baichuan_table_name,
|
||||
})
|
||||
|
||||
|
||||
def push_market_value():
|
||||
logger.info('发送预测结果到市场信息平台')
|
||||
# 读取预测数据和模型评估数据
|
||||
predict_file_path = os.path.join(config.dataset, 'predict.csv')
|
||||
model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
|
||||
try:
|
||||
predictdata_df = pd.read_csv(predict_file_path)
|
||||
top_models_df = pd.read_csv(model_eval_file_path)
|
||||
except FileNotFoundError as e:
|
||||
logger.error(f"文件未找到: {e}")
|
||||
return
|
||||
|
||||
predictdata = predictdata_df.copy()
|
||||
|
||||
# 取模型前十
|
||||
top_models = top_models_df['模型(Model)'].head(10).tolist()
|
||||
# 去掉FDBformer
|
||||
if 'FEDformer' in top_models:
|
||||
top_models.remove('FEDformer')
|
||||
# 计算前十模型的均值
|
||||
predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
|
||||
|
||||
# 打印日期和前十模型均值
|
||||
print(predictdata_df[['ds', 'top_models_mean']])
|
||||
|
||||
# 准备要推送的数据
|
||||
ciyue_mean = predictdata_df['top_models_mean'].iloc[0]
|
||||
cieryue_mean = predictdata_df['top_models_mean'].iloc[1]
|
||||
cisanyue_mean = predictdata_df['top_models_mean'].iloc[2]
|
||||
cisieryue_mean = predictdata_df['top_models_mean'].iloc[3]
|
||||
# 保留两位小数
|
||||
ciyue_mean = round(ciyue_mean, 2)
|
||||
cieryue_mean = round(cieryue_mean, 2)
|
||||
cisanyue_mean = round(cisanyue_mean, 2)
|
||||
cisieryue_mean = round(cisieryue_mean, 2)
|
||||
|
||||
predictdata = [
|
||||
{
|
||||
"dataItemNo": global_config['bdwd_items']['ciyue'],
|
||||
"dataDate": global_config['end_time'].replace('-', ''),
|
||||
"dataStatus": "add",
|
||||
"dataValue": ciyue_mean
|
||||
},
|
||||
{
|
||||
"dataItemNo": global_config['bdwd_items']['cieryue'],
|
||||
"dataDate": global_config['end_time'].replace('-', ''),
|
||||
"dataStatus": "add",
|
||||
"dataValue": cieryue_mean
|
||||
},
|
||||
{
|
||||
"dataItemNo": global_config['bdwd_items']['cisanyue'],
|
||||
"dataDate": global_config['end_time'].replace('-', ''),
|
||||
"dataStatus": "add",
|
||||
"dataValue": cisanyue_mean
|
||||
},
|
||||
{
|
||||
"dataItemNo": global_config['bdwd_items']['cisiyue'],
|
||||
"dataDate": global_config['end_time'].replace('-', ''),
|
||||
"dataStatus": "add",
|
||||
"dataValue": cisieryue_mean
|
||||
}
|
||||
]
|
||||
|
||||
print(predictdata)
|
||||
|
||||
# 推送数据到市场信息平台
|
||||
try:
|
||||
push_market_data(predictdata)
|
||||
except Exception as e:
|
||||
logger.error(f"推送数据失败: {e}")
|
||||
|
||||
|
||||
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
|
||||
"""
|
||||
|
||||
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)
|
||||
|
||||
except:
|
||||
logger.info('最高最低价拼接失败')
|
||||
|
||||
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'] = '石油焦对标炼厂价格'
|
||||
df_baichuanliebiao['频度'] = '其他'
|
||||
df_zhibiaoliebiao = pd.concat(
|
||||
[df_zhibiaoliebiao, df_baichuanliebiao], axis=0)
|
||||
|
||||
# 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(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['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(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()
|
||||
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}')
|
||||
|
||||
# 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:-2]
|
||||
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)
|
||||
|
||||
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=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)
|
||||
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 = '石油焦大模型普货渠道.pdf' # 报告文件名
|
||||
# reportname = f'石油焦大模型普货月度预测--{end_time}.pdf' # 报告文件名
|
||||
# reportname = reportname.replace(':', '-') # 替换冒号
|
||||
shiyoujiao_puhuo_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()
|
||||
|
||||
# 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
|
||||
# # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
# for i_time in pd.date_range('2024-12-1', '2025-2-26', freq='W'):
|
||||
# end_time = i_time.strftime('%Y-%m-%d')
|
||||
# predict_main()
|
||||
|
||||
predict_main()
|
||||
424
main_shiyoujiao_puhuo_zhoudu.py
Normal file
424
main_shiyoujiao_puhuo_zhoudu.py
Normal file
@ -0,0 +1,424 @@
|
||||
# 读取配置
|
||||
|
||||
from lib.dataread import *
|
||||
from config_shiyoujiao_lvyong_zhoudu import *
|
||||
from lib.tools import SendMail, exception_logger
|
||||
from models.nerulforcastmodels import ex_Model, model_losss, shiyoujiao_lvyong_export_pdf
|
||||
import datetime
|
||||
import torch
|
||||
torch.set_float32_matmul_precision("high")
|
||||
|
||||
global_config.update({
|
||||
# 核心参数
|
||||
'logger': logger,
|
||||
'dataset': dataset,
|
||||
'y': y,
|
||||
'is_debug': is_debug,
|
||||
'is_train': is_train,
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'is_update_report': is_update_report,
|
||||
'settings': settings,
|
||||
'weight_dict': weight_dict,
|
||||
'baichuanidnamedict': baichuanidnamedict,
|
||||
'bdwdname': bdwdname,
|
||||
|
||||
|
||||
# 模型参数
|
||||
'data_set': data_set,
|
||||
'input_size': input_size,
|
||||
'horizon': horizon,
|
||||
'train_steps': train_steps,
|
||||
'val_check_steps': val_check_steps,
|
||||
'val_size': val_size,
|
||||
'test_size': test_size,
|
||||
'modelsindex': modelsindex,
|
||||
'rote': rote,
|
||||
'bdwd_items': bdwd_items,
|
||||
'baichuanidnamedict': baichuanidnamedict,
|
||||
|
||||
# 特征工程开关
|
||||
'is_del_corr': is_del_corr,
|
||||
'is_del_tow_month': is_del_tow_month,
|
||||
'is_eta': is_eta,
|
||||
'is_update_eta': is_update_eta,
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'is_update_predict_value': is_update_predict_value,
|
||||
'early_stop_patience_steps': early_stop_patience_steps,
|
||||
|
||||
# 时间参数
|
||||
'start_year': start_year,
|
||||
'end_time': end_time or datetime.datetime.now().strftime("%Y-%m-%d"),
|
||||
'freq': freq, # 保持列表结构
|
||||
|
||||
# 接口配置
|
||||
'login_pushreport_url': login_pushreport_url,
|
||||
'login_data': login_data,
|
||||
'upload_url': upload_url,
|
||||
'upload_data': upload_data,
|
||||
'upload_warning_url': upload_warning_url,
|
||||
'warning_data': warning_data,
|
||||
|
||||
# 查询接口
|
||||
'query_data_list_item_nos_url': query_data_list_item_nos_url,
|
||||
'query_data_list_item_nos_data': query_data_list_item_nos_data,
|
||||
|
||||
# 上传数据项
|
||||
'push_data_value_list_url': push_data_value_list_url,
|
||||
'push_data_value_list_data': push_data_value_list_data,
|
||||
|
||||
# eta 配置
|
||||
'APPID': APPID,
|
||||
'SECRET': SECRET,
|
||||
'etadata': data,
|
||||
'edbcodelist': edbcodelist,
|
||||
'ClassifyId': ClassifyId,
|
||||
'edbcodedataurl': edbcodedataurl,
|
||||
'classifyidlisturl': classifyidlisturl,
|
||||
'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,
|
||||
})
|
||||
|
||||
|
||||
def push_market_value():
|
||||
logger.info('发送预测结果到市场信息平台')
|
||||
# 读取预测数据和模型评估数据
|
||||
predict_file_path = os.path.join(config.dataset, 'predict.csv')
|
||||
model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
|
||||
try:
|
||||
predictdata_df = pd.read_csv(predict_file_path)
|
||||
top_models_df = pd.read_csv(model_eval_file_path)
|
||||
except FileNotFoundError as e:
|
||||
logger.error(f"文件未找到: {e}")
|
||||
return
|
||||
|
||||
predictdata = predictdata_df.copy()
|
||||
|
||||
# 取模型前十
|
||||
top_models = top_models_df['模型(Model)'].head(10).tolist()
|
||||
# 去掉FDBformer
|
||||
if 'FEDformer' in top_models:
|
||||
top_models.remove('FEDformer')
|
||||
# 计算前十模型的均值
|
||||
predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
|
||||
|
||||
# 打印日期和前十模型均值
|
||||
print(predictdata_df[['ds', 'top_models_mean']])
|
||||
|
||||
# 准备要推送的数据
|
||||
first_mean = predictdata_df['top_models_mean'].iloc[0]
|
||||
last_mean = predictdata_df['top_models_mean'].iloc[-1]
|
||||
# 保留两位小数
|
||||
first_mean = round(first_mean, 2)
|
||||
last_mean = round(last_mean, 2)
|
||||
|
||||
predictdata = [
|
||||
{
|
||||
"dataItemNo": global_config['bdwd_items']['cizhou'],
|
||||
"dataDate": global_config['end_time'].replace('-', ''),
|
||||
"dataStatus": "add",
|
||||
"dataValue": first_mean
|
||||
},
|
||||
{
|
||||
"dataItemNo": global_config['bdwd_items']['gezhou'],
|
||||
"dataDate": global_config['end_time'].replace('-', ''),
|
||||
"dataStatus": "add",
|
||||
"dataValue": last_mean
|
||||
}
|
||||
]
|
||||
|
||||
print(predictdata)
|
||||
|
||||
# 推送数据到市场信息平台
|
||||
try:
|
||||
push_market_data(predictdata)
|
||||
except Exception as e:
|
||||
logger.error(f"推送数据失败: {e}")
|
||||
|
||||
|
||||
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
|
||||
"""
|
||||
|
||||
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)
|
||||
|
||||
except:
|
||||
logger.info('最高最低价拼接失败')
|
||||
|
||||
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'] = '石油焦对标炼厂价格'
|
||||
df_baichuanliebiao['频度'] = '其他'
|
||||
df_zhibiaoliebiao = pd.concat(
|
||||
[df_zhibiaoliebiao, df_baichuanliebiao], axis=0)
|
||||
|
||||
# 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 = zhoududatachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['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(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()
|
||||
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}')
|
||||
|
||||
# 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:-2]
|
||||
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)
|
||||
|
||||
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=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)
|
||||
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 = '石油焦大模型铝用渠道.pdf' # 报告文件名
|
||||
# reportname = f'石油焦铝用大模型周度预测--{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,
|
||||
reportname=reportname, sqlitedb=sqlitedb),
|
||||
|
||||
logger.info('制作报告end')
|
||||
logger.info('模型训练完成')
|
||||
|
||||
push_market_value()
|
||||
|
||||
# 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
|
||||
# # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
# for i_time in pd.date_range('2024-12-1', '2025-2-26', freq='W'):
|
||||
# end_time = i_time.strftime('%Y-%m-%d')
|
||||
# predict_main()
|
||||
|
||||
predict_main()
|
||||
Loading…
Reference in New Issue
Block a user