2025年6月4日原油发版
This commit is contained in:
workpc
parent
046ce2ea1f
commit
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"1 3249.93 1 4876.81 6533.09 \n",
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"昨日计划提货偏差改之前 793.84\n",
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"昨日计划提货偏差改之后 1656.2806\n",
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"**************************************************预测结果： 3622.23\n"
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"**************************************************预测结果： 3622.23\n",
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"更新前一天数据\n",
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"更新数据前\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1441 2025-05-19 3600 34.8154 1 793.84 6000 110418 3630 3236.47 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1441 1 4876.81 6533.09 \n",
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"日期存在，即将更新\n",
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"新数据 [3620.0, '', '', 793.84, '', 110418.01, '', 3249.9268, '', '', 6533.09]\n",
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"更新数据后\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1441 2025-05-19 3600 34.8154 1 793.84 6000 110418 3630 3236.47 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1441 1 4876.81 6533.09 \n",
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"20250520\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1440 2025-05-18 3620 31.4597 1 793.84 6000 110418 3630 3249.93 \n",
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"1441 2025-05-19 3600 31.4597 1 1246.01 6000 111150 3630 3236.47 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1440 1 4876.81 6533.09 \n",
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"1441 1 4876.81 6817.39 \n",
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"前一天的 3249.9268 <class 'float'>\n",
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"现在的 3236.4706 <class 'float'>\n",
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" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
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"0 1440 2025-05-18 3620 31.4597 1 793.84 6000 110418 3630 \n",
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"1 1441 2025-05-19 3600 31.4597 1 1246.01 6000 111150 3630 \n",
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"\n",
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" 即期成本 订单结构 计划产量 京博产量 \n",
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"0 3249.93 1 4876.81 6533.09 \n",
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"1 3236.47 1 4876.81 6817.39 \n",
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"昨日计划提货偏差改之前 1246.01\n",
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"昨日计划提货偏差改之后 1940.5766000000003\n",
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"**************************************************预测结果： 3622.81\n",
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"更新前一天数据\n",
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"更新数据前\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1442 2025-05-20 3600 31.4597 1 1246.01 6000 111150 3630 3248.42 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1442 1 4876.81 6817.39 \n",
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"日期存在，即将更新\n",
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"新数据 [3600.0, 31.4597, '', 1246.01, '', 111149.96, '', 3236.4706, '', '', 6817.386]\n",
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"更新数据后\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1442 2025-05-20 3600 31.4597 1 1246.01 6000 111150 3630 3248.42 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1442 1 4876.81 6817.39 \n",
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"20250521\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1441 2025-05-19 3600 31.4597 1 1246.01 6000 111150 3630 3236.47 \n",
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"1442 2025-05-20 3600 28.104 1 1406.68 6000 111294 3630 3248.42 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1441 1 4876.81 6817.39 \n",
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"1442 1 4876.81 6772.02 \n",
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"前一天的 3236.4706 <class 'float'>\n",
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"现在的 3248.4206 <class 'float'>\n",
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" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
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"0 1441 2025-05-19 3600 31.4597 1 1246.01 6000 111150 3630 \n",
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"1 1442 2025-05-20 3600 28.104 1 1406.68 6000 111294 3630 \n",
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"\n",
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" 即期成本 订单结构 计划产量 京博产量 \n",
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"0 3236.47 1 4876.81 6817.39 \n",
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"1 3248.42 1 4876.81 6772.02 \n",
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"昨日计划提货偏差改之前 1406.68\n",
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"昨日计划提货偏差改之后 1895.2120999999997\n",
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"**************************************************预测结果： 3603.2\n",
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"更新前一天数据\n",
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"更新数据前\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1443 2025-05-21 3600 28.104 1 1406.68 6000 111294 3630 3303.26 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1443 1 4876.81 6772.02 \n",
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"日期存在，即将更新\n",
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"新数据 [3600.0, 28.104, '', 1406.68, '', 111293.57, '', 3248.4206, '', '', 6772.0215]\n",
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"更新数据后\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1443 2025-05-21 3600 28.104 1 1406.68 6000 111294 3630 3303.26 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1443 1 4876.81 6772.02 \n",
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"20250522\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1442 2025-05-20 3600 28.104 1 1406.68 6000 111294 3630 3248.42 \n",
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"1443 2025-05-21 3600 28.104 1 265.5 6000 110523 3630 3303.26 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1442 1 4876.81 6772.02 \n",
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"1443 1 4876.81 7338.12 \n",
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"前一天的 3248.4206 <class 'float'>\n",
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"现在的 3303.2591 <class 'float'>\n",
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" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
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"0 1442 2025-05-20 3600 28.104 1 1406.68 6000 111294 3630 \n",
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"1 1443 2025-05-21 3600 28.104 1 265.5 6000 110523 3630 \n",
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"\n",
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" 即期成本 订单结构 计划产量 京博产量 \n",
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"0 3248.42 1 4876.81 6772.02 \n",
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"1 3303.26 1 4876.81 7338.12 \n",
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"昨日计划提货偏差改之前 265.5\n",
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"昨日计划提货偏差改之后 2461.3066\n",
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"**************************************************预测结果： 3604.08\n",
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"更新前一天数据\n",
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"更新数据前\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1444 2025-05-22 3600 28.104 1 265.5 6000 110523 3630 -61269.9 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1444 1 4876.81 7338.12 \n",
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"日期存在，即将更新\n",
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"新数据 [3600.0, 28.104, '', 265.5, '', 110522.98, '', 3303.2591, '', '', 7338.116]\n",
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"更新数据后\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1444 2025-05-22 3600 28.104 1 265.5 6000 110523 3630 -61269.9 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1444 1 4876.81 7338.12 \n",
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"20250523\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1443 2025-05-21 3600 28.104 1 265.5 6000 110523 3630 3303.26 \n",
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"1444 2025-05-22 3600 24.7483 1 2446.95 6000 110929 3630 -61269.9 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1443 1 4876.81 7338.12 \n",
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"1444 1 4876.81 7444.25 \n",
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"前一天的 3303.2591 <class 'float'>\n",
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"现在的 -61269.9011 <class 'float'>\n",
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"修改了\n",
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" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
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"0 1443 2025-05-21 3600 28.104 1 265.5 6000 110523 3630 \n",
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"1 1444 2025-05-22 3600 24.7483 1 2446.95 6000 110929 3630 \n",
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"\n",
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" 即期成本 订单结构 计划产量 京博产量 \n",
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"0 3303.26 1 4876.81 7338.12 \n",
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"1 3303.26 1 4876.81 7444.25 \n",
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"昨日计划提货偏差改之前 2446.95\n",
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"昨日计划提货偏差改之后 2567.4371\n",
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"**************************************************预测结果： 3604.24\n",
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"更新前一天数据\n",
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"更新数据前\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1445 2025-05-23 3600 24.7483 1 2446.95 6000 110929 3630 -65021.4 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1445 1 4876.81 7444.25 \n",
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"日期存在，即将更新\n",
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"新数据 [3600.0, 24.7483, '', 2446.95, '', 110928.95, '', -61269.9011, '', '', 7444.2465]\n",
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"更新数据后\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1445 2025-05-23 3600 24.7483 1 2446.95 6000 110929 3630 -65021.4 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1445 1 4876.81 7444.25 \n",
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"20250524\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1444 2025-05-22 3600 24.7483 1 2446.95 6000 110929 3630 -61269.9 \n",
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"1445 2025-05-23 3600 26.0067 1 318.59 6000 109961 3630 -65021.4 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1444 1 4876.81 7444.25 \n",
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"1445 1 4876.81 7546.95 \n",
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"前一天的 -61269.9011 <class 'float'>\n",
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"现在的 -65021.4299 <class 'float'>\n",
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"修改了\n",
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" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
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"0 1444 2025-05-22 3600 24.7483 1 2446.95 6000 110929 3630 \n",
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"1 1445 2025-05-23 3600 26.0067 1 318.59 6000 109961 3630 \n",
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"\n",
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" 即期成本 订单结构 计划产量 京博产量 \n",
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"0 -61269.9 1 4876.81 7444.25 \n",
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"1 -61269.9 1 4876.81 7546.95 \n",
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"昨日计划提货偏差改之前 318.59\n",
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"昨日计划提货偏差改之后 2670.1436000000003\n",
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"**************************************************预测结果： 3604.4\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"更新前一天数据\n",
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"更新数据前\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1446 2025-05-24 3600 26.0067 1 318.59 6000 109961 3630 -70579.8 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1446 1 4876.81 7546.95 \n",
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"日期存在，即将更新\n",
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"新数据 [3600.0, 26.0067, '', 318.59, '', 109961.08, '', -65021.4299, '', '', 7546.953]\n",
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"更新数据后\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1446 2025-05-24 3600 26.0067 1 318.59 6000 109961 3630 -70579.8 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1446 1 4876.81 7546.95 \n",
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"20250525\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1445 2025-05-23 3600 26.0067 1 318.59 6000 109961 3630 -65021.4 \n",
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"1446 2025-05-24 3600 26.0067 1 2949.09 6000 110078 3630 -70579.8 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
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"1445 1 4876.81 7546.95 \n",
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"1446 1 4876.81 6911.72 \n",
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"前一天的 -65021.4299 <class 'float'>\n",
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"现在的 -70579.7584 <class 'float'>\n",
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"修改了\n",
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" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
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"0 1445 2025-05-23 3600 26.0067 1 318.59 6000 109961 3630 \n",
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"1 1446 2025-05-24 3600 26.0067 1 2949.09 6000 110078 3630 \n",
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"\n",
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" 即期成本 订单结构 计划产量 京博产量 \n",
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"0 -65021.4 1 4876.81 7546.95 \n",
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"1 -65021.4 1 4876.81 6911.72 \n",
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"昨日计划提货偏差改之前 2949.09\n",
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"昨日计划提货偏差改之后 2034.9106000000002\n",
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"**************************************************预测结果： 3603.41\n",
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"更新前一天数据\n",
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"更新数据前\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
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"1447 2025-05-25 3600 26.0067 1 2949.09 6000 110078 3630 -65108.7 \n",
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"\n",
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" 订单结构 计划产量 京博产量 \n",
|
||||
"1447 1 4876.81 6911.72 \n",
|
||||
"日期存在，即将更新\n",
|
||||
"新数据 [3600.0, '', '', 2949.09, '', 110078.22, '', -70579.7584, '', '', 6911.72]\n",
|
||||
"更新数据后\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1447 2025-05-25 3600 26.0067 1 2949.09 6000 110078 3630 -65108.7 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1447 1 4876.81 6911.72 \n",
|
||||
"20250526\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1446 2025-05-24 3600 26.0067 1 2949.09 6000 110078 3630 -70579.8 \n",
|
||||
"1447 2025-05-25 3600 26.0067 1 3076.16 6000 110169 3630 -65108.7 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1446 1 4876.81 6911.72 \n",
|
||||
"1447 1 4876.81 6912.92 \n",
|
||||
"前一天的 -70579.7584 <class 'float'>\n",
|
||||
"现在的 -65108.6941 <class 'float'>\n",
|
||||
"修改了\n",
|
||||
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
|
||||
"0 1446 2025-05-24 3600 26.0067 1 2949.09 6000 110078 3630 \n",
|
||||
"1 1447 2025-05-25 3600 26.0067 1 3076.16 6000 110169 3630 \n",
|
||||
"\n",
|
||||
" 即期成本 订单结构 计划产量 京博产量 \n",
|
||||
"0 -70579.8 1 4876.81 6911.72 \n",
|
||||
"1 -70579.8 1 4876.81 6912.92 \n",
|
||||
"昨日计划提货偏差改之前 3076.16\n",
|
||||
"昨日计划提货偏差改之后 2036.1106\n",
|
||||
"**************************************************预测结果： 3603.42\n",
|
||||
"更新前一天数据\n",
|
||||
"更新数据前\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1448 2025-05-26 3600 26.0067 1 3076.16 6000 110169 3630 -62606.2 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1448 1 4876.81 6912.92 \n",
|
||||
"日期存在，即将更新\n",
|
||||
"新数据 [3600.0, '', '', 3076.16, '', 110168.92, '', -65108.6941, '', '', 6912.92]\n",
|
||||
"更新数据后\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1448 2025-05-26 3600 26.0067 1 3076.16 6000 110169 3630 -62606.2 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1448 1 4876.81 6912.92 \n",
|
||||
"20250527\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1447 2025-05-25 3600 28.943 1 3076.16 6000 110169 3630 -65108.7 \n",
|
||||
"1448 2025-05-26 3600 28.104 1 -1096.24 6000 110398 3630 -62606.2 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1447 1 4876.81 6912.92 \n",
|
||||
"1448 1 4876.81 7359.64 \n",
|
||||
"前一天的 -65108.6941 <class 'float'>\n",
|
||||
"现在的 -62606.1562 <class 'float'>\n",
|
||||
"修改了\n",
|
||||
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
|
||||
"0 1447 2025-05-25 3600 28.943 1 3076.16 6000 110169 3630 \n",
|
||||
"1 1448 2025-05-26 3600 28.104 1 -1096.24 6000 110398 3630 \n",
|
||||
"\n",
|
||||
" 即期成本 订单结构 计划产量 京博产量 \n",
|
||||
"0 -65108.7 1 4876.81 6912.92 \n",
|
||||
"1 -65108.7 1 4876.81 7359.64 \n",
|
||||
"昨日计划提货偏差改之前 -1096.24\n",
|
||||
"昨日计划提货偏差改之后 2482.8306000000002\n",
|
||||
"**************************************************预测结果： 3604.11\n",
|
||||
"更新前一天数据\n",
|
||||
"更新数据前\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1449 2025-05-27 3600 28.104 1 -1096.24 6000 110398 3630 -63415.4 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1449 1 4876.81 7359.64 \n",
|
||||
"日期存在，即将更新\n",
|
||||
"新数据 [3600.0, 28.104, '', -1096.24, '', 110398.08, '', -62606.1562, '', '', 7359.64]\n",
|
||||
"更新数据后\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1449 2025-05-27 3600 28.104 1 -1096.24 6000 110398 3630 -63415.4 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1449 1 4876.81 7359.64 \n",
|
||||
"20250528\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1448 2025-05-26 3600 28.104 1 -1096.24 6000 110398 3630 -62606.2 \n",
|
||||
"1449 2025-05-27 3600 31.4597 1 1608.74 6000 109292 3630 -63415.4 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1448 1 4876.81 7359.64 \n",
|
||||
"1449 1 4876.81 7709.12 \n",
|
||||
"前一天的 -62606.1562 <class 'float'>\n",
|
||||
"现在的 -63415.4022 <class 'float'>\n",
|
||||
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
|
||||
"0 1448 2025-05-26 3600 28.104 1 -1096.24 6000 110398 3630 \n",
|
||||
"1 1449 2025-05-27 3600 31.4597 1 1608.74 6000 109292 3630 \n",
|
||||
"\n",
|
||||
" 即期成本 订单结构 计划产量 京博产量 \n",
|
||||
"0 -62606.2 1 4876.81 7359.64 \n",
|
||||
"1 -63415.4 1 4876.81 7709.12 \n",
|
||||
"昨日计划提货偏差改之前 1608.74\n",
|
||||
"昨日计划提货偏差改之后 2832.3106\n",
|
||||
"**************************************************预测结果： 3442.34\n",
|
||||
"更新前一天数据\n",
|
||||
"更新数据前\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1450 2025-05-28 3600 31.4597 1 1608.74 6000 109292 3630 3045.17 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1450 1 4876.81 7709.12 \n",
|
||||
"日期存在，即将更新\n",
|
||||
"新数据 [3600.0, 31.4597, '', 1608.74, '', 109291.7, '', -63415.4022, '', '', 7709.12]\n",
|
||||
"更新数据后\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1450 2025-05-28 3600 31.4597 1 1608.74 6000 109292 3630 3045.17 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1450 1 4876.81 7709.12 \n",
|
||||
"20250529\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1449 2025-05-27 3600 31.4597 1 1608.74 6000 109292 3630 -63415.4 \n",
|
||||
"1450 2025-05-28 3620 32.2987 1 -1155.78 6000 109867 3630 3045.17 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1449 1 4876.81 7709.12 \n",
|
||||
"1450 1 4876.81 7515.76 \n",
|
||||
"前一天的 -63415.4022 <class 'float'>\n",
|
||||
"现在的 3045.1686 <class 'float'>\n",
|
||||
"修改了\n",
|
||||
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
|
||||
"0 1449 2025-05-27 3600 31.4597 1 1608.74 6000 109292 3630 \n",
|
||||
"1 1450 2025-05-28 3620 32.2987 1 -1155.78 6000 109867 3630 \n",
|
||||
"\n",
|
||||
" 即期成本 订单结构 计划产量 京博产量 \n",
|
||||
"0 -63415.4 1 4876.81 7709.12 \n",
|
||||
"1 -63415.4 1 4876.81 7515.76 \n",
|
||||
"昨日计划提货偏差改之前 -1155.78\n",
|
||||
"昨日计划提货偏差改之后 2638.9506\n",
|
||||
"**************************************************预测结果： 3603.86\n",
|
||||
"更新前一天数据\n",
|
||||
"更新数据前\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1451 2025-05-29 3620 32.2987 1 -1155.78 6000 109867 3630 3142.84 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1451 1 4876.81 7515.76 \n",
|
||||
"日期存在，即将更新\n",
|
||||
"新数据 [3620.0, 32.2987, '', -1155.78, '', 109866.97, '', 3045.1686, '', '', 7515.76]\n",
|
||||
"更新数据后\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1451 2025-05-29 3620 32.2987 1 -1155.78 6000 109867 3630 3142.84 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1451 1 4876.81 7515.76 \n",
|
||||
"20250530\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1450 2025-05-28 3620 32.2987 1 -1155.78 6000 109867 3630 3045.17 \n",
|
||||
"1451 2025-05-29 3620 31.4597 1 1468.98 6000 110016 3630 3142.84 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1450 1 4876.81 7515.76 \n",
|
||||
"1451 1 4876.81 7321.16 \n",
|
||||
"前一天的 3045.1686 <class 'float'>\n",
|
||||
"现在的 3142.8424 <class 'float'>\n",
|
||||
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
|
||||
"0 1450 2025-05-28 3620 32.2987 1 -1155.78 6000 109867 3630 \n",
|
||||
"1 1451 2025-05-29 3620 31.4597 1 1468.98 6000 110016 3630 \n",
|
||||
"\n",
|
||||
" 即期成本 订单结构 计划产量 京博产量 \n",
|
||||
"0 3045.17 1 4876.81 7515.76 \n",
|
||||
"1 3142.84 1 4876.81 7321.16 \n",
|
||||
"昨日计划提货偏差改之前 1468.98\n",
|
||||
"昨日计划提货偏差改之后 2444.3505999999998\n",
|
||||
"**************************************************预测结果： 3623.59\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"更新前一天数据\n",
|
||||
"更新数据前\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1452 2025-05-30 3650 31.4597 1 1468.98 6000 110016 3630 3002.11 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1452 1 4876.81 7321.16 \n",
|
||||
"日期存在，即将更新\n",
|
||||
"新数据 [3620.0, 31.4597, '', 1468.98, '', 110015.78, '', 3142.8424, '', '', 7321.16]\n",
|
||||
"更新数据后\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1452 2025-05-30 3650 31.4597 1 1468.98 6000 110016 3630 3002.11 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1452 1 4876.81 7321.16 \n",
|
||||
"20250531\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1451 2025-05-29 3620 31.4597 1 1468.98 6000 110016 3630 3142.84 \n",
|
||||
"1452 2025-05-30 3650 31.4597 1 200.53 6000 109452 3630 3002.11 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1451 1 4876.81 7321.16 \n",
|
||||
"1452 1 4876.81 7210.15 \n",
|
||||
"前一天的 3142.8424 <class 'float'>\n",
|
||||
"现在的 3002.1107 <class 'float'>\n",
|
||||
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
|
||||
"0 1451 2025-05-29 3620 31.4597 1 1468.98 6000 110016 3630 \n",
|
||||
"1 1452 2025-05-30 3650 31.4597 1 200.53 6000 109452 3630 \n",
|
||||
"\n",
|
||||
" 即期成本 订单结构 计划产量 京博产量 \n",
|
||||
"0 3142.84 1 4876.81 7321.16 \n",
|
||||
"1 3002.11 1 4876.81 7210.15 \n",
|
||||
"昨日计划提货偏差改之前 200.53\n",
|
||||
"昨日计划提货偏差改之后 2333.3405999999995\n",
|
||||
"**************************************************预测结果： 3595.27\n",
|
||||
"更新前一天数据\n",
|
||||
"更新数据前\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1453 2025-05-31 3650 31.4597 1 200.53 6000 109452 3630 2931.21 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1453 1 4876.81 7210.15 \n",
|
||||
"日期存在，即将更新\n",
|
||||
"新数据 [3650.0, 31.4597, '', 200.53, '', 109452.46, '', 3002.1107, '', '', 7210.15]\n",
|
||||
"更新数据后\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1453 2025-05-31 3650 31.4597 1 200.53 6000 109452 3630 2931.21 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1453 1 4876.81 7210.15 \n",
|
||||
"20250601\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1452 2025-05-30 3650 31.4597 1 200.53 6000 109452 3630 3002.11 \n",
|
||||
"1453 2025-05-31 3650 31.4597 1 200.53 6000 109452 3630 2931.21 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1452 1 4876.81 7210.15 \n",
|
||||
"1453 1 4876.81 7210.15 \n",
|
||||
"前一天的 3002.1107 <class 'float'>\n",
|
||||
"现在的 2931.2118 <class 'float'>\n",
|
||||
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
|
||||
"0 1452 2025-05-30 3650 31.4597 1 200.53 6000 109452 3630 \n",
|
||||
"1 1453 2025-05-31 3650 31.4597 1 200.53 6000 109452 3630 \n",
|
||||
"\n",
|
||||
" 即期成本 订单结构 计划产量 京博产量 \n",
|
||||
"0 3002.11 1 4876.81 7210.15 \n",
|
||||
"1 2931.21 1 4876.81 7210.15 \n",
|
||||
"昨日计划提货偏差改之前 200.53\n",
|
||||
"昨日计划提货偏差改之后 2333.3405999999995\n",
|
||||
"**************************************************预测结果： 3653.42\n",
|
||||
"更新前一天数据\n",
|
||||
"更新数据前\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1454 2025-06-01 3650 31.4597 1 200.53 6000 109452 3630 2940.43 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1454 1 4876.81 7210.15 \n",
|
||||
"日期存在，即将更新\n",
|
||||
"新数据 [3650.0, '', '', 4837.68, '', 109979.05, '', 2931.2118, '', '', 6263.17]\n",
|
||||
"更新数据后\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1454 2025-06-01 3650 31.4597 1 200.53 6000 109452 3630 2940.43 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1454 1 4876.81 7210.15 \n",
|
||||
"20250602\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1453 2025-05-31 3650 31.4597 1 4837.68 6000 109979 3630 2931.21 \n",
|
||||
"1454 2025-06-01 3620 31.4597 1 2998.04 6000 112404 3630 2940.43 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1453 1 4876.81 6263.17 \n",
|
||||
"1454 1 4876.81 8453.8 \n",
|
||||
"前一天的 2931.2118 <class 'float'>\n",
|
||||
"现在的 2940.4318 <class 'float'>\n",
|
||||
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
|
||||
"0 1453 2025-05-31 3650 31.4597 1 4837.68 6000 109979 3630 \n",
|
||||
"1 1454 2025-06-01 3620 31.4597 1 2998.04 6000 112404 3630 \n",
|
||||
"\n",
|
||||
" 即期成本 订单结构 计划产量 京博产量 \n",
|
||||
"0 2931.21 1 4876.81 6263.17 \n",
|
||||
"1 2940.43 1 4876.81 8453.8 \n",
|
||||
"昨日计划提货偏差改之前 2998.04\n",
|
||||
"昨日计划提货偏差改之后 3576.9925999999996\n",
|
||||
"**************************************************预测结果： 3655.35\n",
|
||||
"更新前一天数据\n",
|
||||
"更新数据前\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1455 2025-06-02 3620 31.4597 1 2998.04 6000 112404 3630 3088.17 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1455 1 4876.81 8453.8 \n",
|
||||
"日期存在，即将更新\n",
|
||||
"新数据 [3620.0, '', '', 2998.04, '', 112404.0, '', 2940.4318, '', '', 8453.802]\n",
|
||||
"更新数据后\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1455 2025-06-02 3620 31.4597 1 2998.04 6000 112404 3630 3088.17 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1455 1 4876.81 8453.8 \n",
|
||||
"20250603\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1454 2025-06-01 3620 31.4597 1 2998.04 6000 112404 3630 2940.43 \n",
|
||||
"1455 2025-06-02 3650 31.4597 1 -361.55 6000 113137 3630 3088.17 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1454 1 4876.81 8453.8 \n",
|
||||
"1455 1 4876.81 7274.36 \n",
|
||||
"前一天的 2940.4318 <class 'float'>\n",
|
||||
"现在的 3088.1711 <class 'float'>\n",
|
||||
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
|
||||
"0 1454 2025-06-01 3620 31.4597 1 2998.04 6000 112404 3630 \n",
|
||||
"1 1455 2025-06-02 3650 31.4597 1 -361.55 6000 113137 3630 \n",
|
||||
"\n",
|
||||
" 即期成本 订单结构 计划产量 京博产量 \n",
|
||||
"0 2940.43 1 4876.81 8453.8 \n",
|
||||
"1 3088.17 1 4876.81 7274.36 \n",
|
||||
"昨日计划提货偏差改之前 -361.55\n",
|
||||
"昨日计划提货偏差改之后 2397.5505999999996\n",
|
||||
"**************************************************预测结果： 3653.07\n",
|
||||
"更新前一天数据\n",
|
||||
"更新数据前\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1456 2025-06-03 3650 31.4597 1 -361.55 6000 113137 3630 3144.69 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1456 1 4876.81 7274.36 \n",
|
||||
"日期存在，即将更新\n",
|
||||
"新数据 [3650.0, '', '', -361.55, '', 113137.24, '', 3088.1711, '', '', 7274.36]\n",
|
||||
"更新数据后\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1456 2025-06-03 3650 31.4597 1 -361.55 6000 113137 3630 3144.69 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1456 1 4876.81 7274.36 \n",
|
||||
"20250604\n",
|
||||
" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 即期成本 \\\n",
|
||||
"1455 2025-06-02 3650 33.557 1 -361.55 6000 113137 3630 3088.17 \n",
|
||||
"1456 2025-06-03 3650 34.8154 1 -676.29 6000 113920 3630 3144.69 \n",
|
||||
"\n",
|
||||
" 订单结构 计划产量 京博产量 \n",
|
||||
"1455 1 4876.81 7274.36 \n",
|
||||
"1456 1 4876.81 7404.7 \n",
|
||||
"前一天的 3088.1711 <class 'float'>\n",
|
||||
"现在的 3144.6905 <class 'float'>\n",
|
||||
" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n",
|
||||
"0 1455 2025-06-02 3650 33.557 1 -361.55 6000 113137 3630 \n",
|
||||
"1 1456 2025-06-03 3650 34.8154 1 -676.29 6000 113920 3630 \n",
|
||||
"\n",
|
||||
" 即期成本 订单结构 计划产量 京博产量 \n",
|
||||
"0 3088.17 1 4876.81 7274.36 \n",
|
||||
"1 3144.69 1 4876.81 7404.7 \n",
|
||||
"昨日计划提货偏差改之前 -676.29\n",
|
||||
"昨日计划提货偏差改之后 2527.8866\n",
|
||||
"**************************************************预测结果： 3653.59\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
1117
aisenzhecode/沥青/沥青定性每日执行.py
Normal file
1117
aisenzhecode/沥青/沥青定性每日执行.py
Normal file
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
718
aisenzhecode/沥青/沥青定量每日执行.py
Normal file
718
aisenzhecode/沥青/沥青定量每日执行.py
Normal file
@ -0,0 +1,718 @@
|
||||
import requests
|
||||
import json
|
||||
from datetime import datetime, timedelta
|
||||
import time
|
||||
import pandas as pd
|
||||
|
||||
# 变量定义
|
||||
login_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
search_url = "http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos"
|
||||
queryDataListItemNos_url = "http://10.200.32.39/jingbo-api//api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
|
||||
login_push_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
upload_url = "http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
login_push_data = {
|
||||
"data": {
|
||||
"account": "api_dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
read_file_path_name = "沥青数据项.xlsx"
|
||||
one_cols = []
|
||||
two_cols = []
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sn
|
||||
import random
|
||||
import time
|
||||
|
||||
from plotly import __version__
|
||||
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
|
||||
|
||||
from sklearn import preprocessing
|
||||
|
||||
from pandas import Series,DataFrame
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import sklearn.datasets as datasets
|
||||
|
||||
#导入机器学习算法模型
|
||||
from sklearn.linear_model import Lasso
|
||||
from xgboost import XGBRegressor
|
||||
|
||||
import statsmodels.api as sm
|
||||
# from keras.preprocessing.sequence import TimeseriesGenerator
|
||||
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
|
||||
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
|
||||
import xgboost as xgb
|
||||
from xgboost import plot_importance, plot_tree
|
||||
from sklearn.metrics import mean_absolute_error
|
||||
from statsmodels.tools.eval_measures import mse,rmse
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from xgboost import XGBRegressor
|
||||
import warnings
|
||||
import pickle
|
||||
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
#切割训练数据和样本数据
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
#用于模型评分
|
||||
from sklearn.metrics import r2_score
|
||||
|
||||
le = preprocessing.LabelEncoder()
|
||||
|
||||
# print(__version__) # requires version >= 1.9.0
|
||||
|
||||
|
||||
import cufflinks as cf
|
||||
cf.go_offline()
|
||||
|
||||
random.seed(100)
|
||||
|
||||
# 数据获取
|
||||
def get_head_auth():
|
||||
login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))
|
||||
text = json.loads(login_res.text)
|
||||
if text["status"]:
|
||||
token = text["data"]["accessToken"]
|
||||
return token
|
||||
else:
|
||||
print("获取认证失败")
|
||||
return None
|
||||
|
||||
|
||||
def get_data_value(token, dataItemNoList,date=''):
|
||||
|
||||
search_data = {
|
||||
"data": {
|
||||
"date": getNow(date)[0],
|
||||
"dataItemNoList": dataItemNoList
|
||||
},
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询"
|
||||
}
|
||||
headers = {"Authorization": token}
|
||||
search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))
|
||||
search_value = json.loads(search_res.text)["data"]
|
||||
if search_value:
|
||||
return search_value
|
||||
else:
|
||||
print("今天没有新数据")
|
||||
return None
|
||||
|
||||
|
||||
# xls文件处理
|
||||
|
||||
def getNow(date='', offset=0):
|
||||
"""生成指定日期的两种格式字符串
|
||||
Args:
|
||||
date: 支持多种输入类型：
|
||||
- datetime对象
|
||||
- 字符串格式（支持'%Y-%m-%d'和'%Y%m%d'）
|
||||
- 空字符串表示当前日期
|
||||
offset: 日期偏移天数
|
||||
Returns:
|
||||
tuple: (紧凑日期字符串, 标准日期字符串)
|
||||
"""
|
||||
# 日期解析逻辑
|
||||
from datetime import datetime,timedelta
|
||||
if isinstance(date, datetime):
|
||||
now = date
|
||||
else:
|
||||
now = datetime.now()
|
||||
if date:
|
||||
# 尝试多种日期格式解析
|
||||
for fmt in ('%Y-%m-%d', '%Y%m%d', '%Y/%m/%d'):
|
||||
try:
|
||||
now = datetime.strptime(str(date), fmt)
|
||||
break
|
||||
except ValueError:
|
||||
continue
|
||||
else:
|
||||
raise ValueError(f"无法解析的日期格式: {date}")
|
||||
|
||||
# 应用日期偏移
|
||||
now = now - timedelta(days=offset)
|
||||
|
||||
# 统一格式化输出
|
||||
date_str = now.strftime("%Y-%m-%d")
|
||||
compact_date = date_str.replace("-", "")
|
||||
return compact_date, date_str
|
||||
|
||||
|
||||
def get_head_push_auth():
|
||||
login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))
|
||||
text = json.loads(login_res.text)
|
||||
if text["status"]:
|
||||
token = text["data"]["accessToken"]
|
||||
return token
|
||||
else:
|
||||
print("获取认证失败")
|
||||
return None
|
||||
|
||||
def upload_data_to_system(token_push,date):
|
||||
data = {
|
||||
"funcModule": "数据表信息列表",
|
||||
"funcOperation": "新增",
|
||||
"data": [
|
||||
{"dataItemNo": "C01100036|Forecast_Price|ACN",
|
||||
"dataDate": getNow(date)[0],
|
||||
"dataStatus": "add",
|
||||
"dataValue": forecast_price()
|
||||
}
|
||||
|
||||
]
|
||||
}
|
||||
headers = {"Authorization": token_push}
|
||||
res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))
|
||||
print(res.text)
|
||||
|
||||
def forecast_price():
|
||||
df_test = pd.read_excel('沥青数据项.xlsx')
|
||||
df_test.drop([0],inplace=True)
|
||||
df_test[['汽油执行价','柴油执行价','齐鲁石化销量','齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',
|
||||
'科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',
|
||||
'布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',
|
||||
'隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',
|
||||
'京博签单量','京博库存量','京博产量','加权平均成交价']] = df_test[['汽油执行价','柴油执行价','齐鲁石化销量',
|
||||
'齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',
|
||||
'科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',
|
||||
'布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',
|
||||
'隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',
|
||||
'京博签单量','京博库存量','京博产量','加权平均成交价']].astype('float')
|
||||
# df_test['日期']=pd.to_datetime(df_test['日期'], format='%d/%m/%Y',infer_datetime_format=True)
|
||||
df_test['日期']=pd.to_datetime(df_test['日期'], format='%Y-%m-%d',infer_datetime_format=True)
|
||||
|
||||
#查看每个特征缺失值数量
|
||||
MisVal_Check=df_test.isnull().sum().sort_values(ascending=False)
|
||||
#去掉缺失值百分比>0.4的特征，去掉这些特征后的新表格命名为df_test_1
|
||||
df_MisVal_Check = pd.DataFrame(MisVal_Check,)#
|
||||
df_MisVal_Check_1=df_MisVal_Check.reset_index()
|
||||
df_MisVal_Check_1.columns=['Variable_Name','Missing_Number']
|
||||
df_MisVal_Check_1['Missing_Number']=df_MisVal_Check_1['Missing_Number']/len(df_test)
|
||||
df_test_1=df_test.drop(df_MisVal_Check_1[df_MisVal_Check_1['Missing_Number']>0.4].Variable_Name,axis = 1)
|
||||
|
||||
#将缺失值补为前一个或者后一个数值
|
||||
df_test_1=df_test_1.fillna(df_test.ffill())
|
||||
df_test_1=df_test_1.fillna(df_test_1.bfill())
|
||||
|
||||
# 选择用于模型训练的列名称
|
||||
col_for_training = df_test_1.columns
|
||||
import joblib
|
||||
Best_model_DalyLGPrice = joblib.load("日度价格预测_最佳模型.pkl")
|
||||
# 最新的一天为最后一行的数据
|
||||
df_test_1_Day = df_test_1.tail(1)
|
||||
# 移除不需要的列
|
||||
df_test_1_Day.index = df_test_1_Day["日期"]
|
||||
df_test_1_Day = df_test_1_Day.drop(["日期"], axis= 1)
|
||||
df_test_1_Day=df_test_1_Day.drop('京博指导价',axis=1)
|
||||
df_test_1_Day=df_test_1_Day.dropna()
|
||||
|
||||
# df_test_1_Day
|
||||
#预测今日价格，显示至小数点后两位
|
||||
Ypredict_Today=Best_model_DalyLGPrice.predict(df_test_1_Day)
|
||||
|
||||
df_test_1_Day['日度预测价格']=Ypredict_Today
|
||||
print(df_test_1_Day['日度预测价格'])
|
||||
a = df_test_1_Day['日度预测价格']
|
||||
a = a[0]
|
||||
a = float(a)
|
||||
a = round(a,2)
|
||||
return a
|
||||
def optimize_Model():
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.impute import SimpleImputer
|
||||
from sklearn.preprocessing import OrdinalEncoder
|
||||
from sklearn.feature_selection import SelectFromModel
|
||||
from sklearn.metrics import mean_squared_error, r2_score
|
||||
|
||||
pd.set_option('display.max_rows',40)
|
||||
pd.set_option('display.max_columns',40)
|
||||
df_test = pd.read_excel('沥青数据项.xlsx')
|
||||
df_test.drop([0],inplace=True)
|
||||
df_test[['汽油执行价','柴油执行价','齐鲁石化销量','齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',
|
||||
'科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',
|
||||
'布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',
|
||||
'隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',
|
||||
'京博签单量','京博库存量','京博产量','加权平均成交价']] = df_test[['汽油执行价','柴油执行价','齐鲁石化销量','齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',
|
||||
'科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',
|
||||
'布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',
|
||||
'隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',
|
||||
'京博签单量','京博库存量','京博产量','加权平均成交价']].astype('float')
|
||||
# df_test = pd.read_csv('定价模型数据收集20190901-20230615.csv',encoding = 'gbk',engine = 'python')
|
||||
# df_test['日期']=pd.to_datetime(df_test['日期'], format='%m/%d/%Y',infer_datetime_format=True)
|
||||
df_test['日期']=pd.to_datetime(df_test['日期'], format='%Y-%m-%d',infer_datetime_format=True)
|
||||
# df_test.tail(3)
|
||||
MisVal_Check=df_test.isnull().sum().sort_values(ascending=False)
|
||||
#去掉缺失值百分比>0.4的特征，去掉这些特征后的新表格命名为df_test_1
|
||||
df_MisVal_Check = pd.DataFrame(MisVal_Check,)#
|
||||
df_MisVal_Check_1=df_MisVal_Check.reset_index()
|
||||
df_MisVal_Check_1.columns=['Variable_Name','Missing_Number']
|
||||
df_MisVal_Check_1['Missing_Number']=df_MisVal_Check_1['Missing_Number']/len(df_test)
|
||||
df_test_1=df_test.drop(df_MisVal_Check_1[df_MisVal_Check_1['Missing_Number']>0.4].Variable_Name,axis = 1)
|
||||
#将缺失值补为前一个或者后一个数值
|
||||
df_test_1=df_test_1.fillna(df_test.ffill())
|
||||
df_test_1=df_test_1.fillna(df_test_1.bfill())
|
||||
df_test_1["日期"] = pd.to_datetime(df_test_1["日期"])
|
||||
df_test_1.index = df_test_1["日期"]
|
||||
df_test_1 = df_test_1.drop(["日期"], axis= 1)
|
||||
dataset1=df_test_1.drop('京博指导价',axis=1)#.astype(float)
|
||||
|
||||
y=df_test_1['京博指导价']
|
||||
|
||||
x=dataset1
|
||||
|
||||
train = x
|
||||
target = y
|
||||
|
||||
#切割数据样本集合测试集
|
||||
X_train,x_test,y_train,y_true = train_test_split(train,target,test_size=0.2,random_state=0)
|
||||
|
||||
|
||||
from sklearn.linear_model import Lasso
|
||||
from xgboost import XGBRegressor
|
||||
|
||||
import statsmodels.api as sm
|
||||
# from keras.preprocessing.sequence import TimeseriesGenerator
|
||||
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
|
||||
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
|
||||
import xgboost as xgb
|
||||
from xgboost import plot_importance, plot_tree
|
||||
from sklearn.metrics import mean_absolute_error
|
||||
from statsmodels.tools.eval_measures import mse,rmse
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from xgboost import XGBRegressor
|
||||
import warnings
|
||||
import pickle
|
||||
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
#切割训练数据和样本数据
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
#用于模型评分
|
||||
from sklearn.metrics import r2_score
|
||||
|
||||
#模型缩写
|
||||
Lasso = Lasso(random_state=0)
|
||||
XGBR = XGBRegressor(random_state=0)
|
||||
Lasso.fit(X_train,y_train)
|
||||
XGBR.fit(X_train,y_train)
|
||||
y_pre_Lasso = Lasso.predict(x_test)
|
||||
y_pre_XGBR = XGBR.predict(x_test)
|
||||
|
||||
#计算Lasso、XGBR、RandomForestR、AdaBoostR、GradientBoostingR、BaggingRegressor各模型的R²
|
||||
Lasso_score = r2_score(y_true,y_pre_Lasso)
|
||||
XGBR_score=r2_score(y_true,y_pre_XGBR)
|
||||
|
||||
#计算Lasso、XGBR的MSE和RMSE
|
||||
Lasso_MSE=mean_squared_error(y_true, y_pre_Lasso)
|
||||
XGBR_MSE=mean_squared_error(y_true, y_pre_XGBR)
|
||||
|
||||
Lasso_RMSE=np.sqrt(Lasso_MSE)
|
||||
XGBR_RMSE=np.sqrt(XGBR_MSE)
|
||||
model_results = pd.DataFrame([['Lasso', Lasso_RMSE, Lasso_score],
|
||||
['XgBoost', XGBR_RMSE, XGBR_score]],
|
||||
columns = ['模型(Model)','均方根误差(RMSE)', 'R^2 score'])
|
||||
model_results1=model_results.set_index('模型(Model)')
|
||||
|
||||
def plot_feature_importance(importance,names,model_type):
|
||||
feature_importance = np.array(importance)
|
||||
feature_names = np.array(names)
|
||||
|
||||
data={'feature_names':feature_names,'feature_importance':feature_importance}
|
||||
fi_df = pd.DataFrame(data)
|
||||
|
||||
fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)
|
||||
|
||||
plt.figure(figsize=(10,8))
|
||||
sn.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])
|
||||
|
||||
plt.title(model_type + " "+'FEATURE IMPORTANCE')
|
||||
plt.xlabel('FEATURE IMPORTANCE')
|
||||
plt.ylabel('FEATURE NAMES')
|
||||
from pylab import mpl
|
||||
mpl.rcParams['font.sans-serif'] = ['SimHei']
|
||||
from xgboost import XGBRegressor
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
|
||||
estimator = XGBRegressor(random_state=0,
|
||||
nthread=4,
|
||||
seed=0
|
||||
)
|
||||
parameters = {
|
||||
'max_depth': range (2, 11, 2), # 树的最大深度
|
||||
'n_estimators': range (50, 101, 10), # 迭代次数
|
||||
'learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]
|
||||
}
|
||||
|
||||
grid_search_XGB = GridSearchCV(
|
||||
estimator=estimator,
|
||||
param_grid=parameters,
|
||||
# n_jobs = 10,
|
||||
cv = 3,
|
||||
verbose=True
|
||||
)
|
||||
|
||||
grid_search_XGB.fit(X_train, y_train)
|
||||
print("Best score: %0.3f" % grid_search_XGB.best_score_)
|
||||
print("Best parameters set:")
|
||||
best_parameters = grid_search_XGB.best_estimator_.get_params()
|
||||
for param_name in sorted(parameters.keys()):
|
||||
print("\t%s: %r" % (param_name, best_parameters[param_name]))
|
||||
y_pred = grid_search_XGB.predict(x_test)
|
||||
|
||||
op_XGBR_score = r2_score(y_true,y_pred)
|
||||
op_XGBR_MSE= mean_squared_error(y_true, y_pred)
|
||||
op_XGBR_RMSE= np.sqrt(op_XGBR_MSE)
|
||||
|
||||
model_results2 = pd.DataFrame([['Optimized_Xgboost', op_XGBR_RMSE, op_XGBR_score]],
|
||||
columns = ['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])
|
||||
model_results2=model_results2.set_index('模型(Model)')
|
||||
|
||||
# results = model_results1.append(model_results2, ignore_index = False)
|
||||
results = pd.concat([model_results1,model_results2],ignore_index=True)
|
||||
results
|
||||
import pickle
|
||||
|
||||
Pkl_Filename = "日度价格预测_最佳模型.pkl"
|
||||
|
||||
with open(Pkl_Filename, 'wb') as file:
|
||||
pickle.dump(grid_search_XGB, file)
|
||||
|
||||
def read_xls_data():
|
||||
"""获取特征项ID"""
|
||||
global one_cols, two_cols
|
||||
# 使用pandas读取Excel文件
|
||||
df = pd.read_excel(read_file_path_name, header=None) # 不自动识别列名
|
||||
# 获取第二行数据（索引为1）
|
||||
one_cols = df.iloc[1].tolist()[1:]
|
||||
print(f'获取到的数据项ID{one_cols}')
|
||||
|
||||
def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd):
|
||||
|
||||
search_data = {
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询",
|
||||
"data": {
|
||||
"dateStart": dateStart,
|
||||
"dateEnd": dateEnd,
|
||||
"dataItemNoList": dataItemNoList # 数据项编码,代表 brent最低价和最高价
|
||||
}
|
||||
}
|
||||
|
||||
headers = {"Authorization": token}
|
||||
search_res = requests.post(url=url, headers=headers, json=search_data, timeout=(3, 5))
|
||||
search_value = json.loads(search_res.text)["data"]
|
||||
if search_value:
|
||||
return search_value
|
||||
else:
|
||||
return None
|
||||
|
||||
|
||||
def save_queryDataListItemNos_xls(data_df,dataItemNoList):
|
||||
from datetime import datetime,timedelta
|
||||
current_year_month = datetime.now().strftime('%Y-%m')
|
||||
grouped = data_df.groupby("dataDate")
|
||||
|
||||
# 使用openpyxl打开xlsx文件
|
||||
from openpyxl import load_workbook
|
||||
workbook = load_workbook('沥青数据项.xlsx')
|
||||
|
||||
# 创建新工作簿
|
||||
new_workbook = load_workbook('沥青数据项.xlsx')
|
||||
|
||||
for sheetname in workbook.sheetnames:
|
||||
sheet = workbook[sheetname]
|
||||
new_sheet = new_workbook[sheetname]
|
||||
|
||||
current_year_month_row = 0
|
||||
# 查找当前月份数据起始行
|
||||
for row_idx, row in enumerate(sheet.iter_rows(values_only=True), 1):
|
||||
if str(row[0]).startswith(current_year_month):
|
||||
current_year_month_row += 1
|
||||
|
||||
# 追加新数据
|
||||
if sheetname == workbook.sheetnames[0]:
|
||||
start_row = sheet.max_row - current_year_month_row + 1
|
||||
for row_idx, (date, group) in enumerate(grouped, start=start_row):
|
||||
new_sheet.cell(row=row_idx, column=1, value=date)
|
||||
for j, dataItemNo in enumerate(dataItemNoList, start=2):
|
||||
if group[group["dataItemNo"] == dataItemNo]["dataValue"].values:
|
||||
new_sheet.cell(row=row_idx, column=j,
|
||||
value=group[group["dataItemNo"] == dataItemNo]["dataValue"].values[0])
|
||||
|
||||
# 保存修改后的xlsx文件
|
||||
new_workbook.save("沥青数据项.xlsx")
|
||||
|
||||
|
||||
# def save_queryDataListItemNos_xls(data_df,dataItemNoList):
|
||||
# from datetime import datetime, timedelta
|
||||
# current_year_month = datetime.now().strftime('%Y-%m')
|
||||
# grouped = data_df.groupby("dataDate")
|
||||
|
||||
# # 打开xls文件
|
||||
# workbook = xlrd.open_workbook('沥青数据项.xlsx')
|
||||
|
||||
# # 获取所有sheet的个数
|
||||
# sheet_count = len(workbook.sheet_names())
|
||||
|
||||
# # 获取所有sheet的名称
|
||||
# sheet_names = workbook.sheet_names()
|
||||
|
||||
# new_workbook = xlwt.Workbook()
|
||||
# for i in range(sheet_count):
|
||||
# # 获取当前sheet
|
||||
# sheet = workbook.sheet_by_index(i)
|
||||
|
||||
# # 获取sheet的行数和列数
|
||||
# row_count = sheet.nrows
|
||||
# col_count = sheet.ncols
|
||||
# # 获取原有数据
|
||||
# data = []
|
||||
# for row in range(row_count):
|
||||
# row_data = []
|
||||
# for col in range(col_count):
|
||||
# row_data.append(sheet.cell_value(row, col))
|
||||
# data.append(row_data)
|
||||
|
||||
# # 创建xlwt的Workbook对象
|
||||
# # 创建sheet
|
||||
# new_sheet = new_workbook.add_sheet(sheet_names[i])
|
||||
|
||||
|
||||
# current_year_month_row = 0
|
||||
# # 将原有的数据写入新的sheet
|
||||
# for row in range(row_count):
|
||||
# for col in range(col_count):
|
||||
# col0 = data[row][0]
|
||||
# # print("col0",col0[:7])
|
||||
# if col0[:7] == current_year_month:
|
||||
# current_year_month_row += 1
|
||||
# break
|
||||
# new_sheet.write(row, col, data[row][col])
|
||||
|
||||
|
||||
# # print("current_year_month_row",current_year_month_row)
|
||||
# if i == 0:
|
||||
# rowFlag = 0
|
||||
# # 查看每组数据
|
||||
# for date, group in grouped:
|
||||
# new_sheet.write(row_count + rowFlag - current_year_month_row, 0, date)
|
||||
# for j in range(len(dataItemNoList)):
|
||||
# dataItemNo = dataItemNoList[j]
|
||||
|
||||
# # for dataItemNo in dataItemNoList:
|
||||
# if group[group["dataItemNo"] == dataItemNo]["dataValue"].values:
|
||||
|
||||
# new_sheet.write(row_count + rowFlag - current_year_month_row, j + 1, group[group["dataItemNo"] == dataItemNo]["dataValue"].values[0])
|
||||
|
||||
# rowFlag += 1
|
||||
|
||||
|
||||
# # 保存新的xls文件
|
||||
# new_workbook.save("沥青数据项.xlsx")
|
||||
|
||||
def queryDataListItemNos(token=None):
|
||||
df = pd.read_excel('沥青数据项.xlsx')
|
||||
dataItemNoList = df.iloc[0].tolist()[1:]
|
||||
|
||||
if token is None:
|
||||
token = get_head_auth()
|
||||
|
||||
if not token:
|
||||
print('token获取失败')
|
||||
return
|
||||
|
||||
# 获取当前日期
|
||||
from datetime import datetime, timedelta
|
||||
current_date = datetime.now()
|
||||
|
||||
# 获取当月1日
|
||||
first_day_of_month = current_date.replace(day=1)
|
||||
|
||||
# 格式化为 YYYYMMDD 格式
|
||||
dateEnd = current_date.strftime('%Y%m%d')
|
||||
dateStart = first_day_of_month.strftime('%Y%m%d')
|
||||
|
||||
search_value = get_queryDataListItemNos_value(token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd)
|
||||
|
||||
|
||||
data_df = pd.DataFrame(search_value)
|
||||
|
||||
data_df["dataDate"] = pd.to_datetime(data_df["dataDate"])
|
||||
data_df["dataDate"] = data_df["dataDate"].dt.strftime('%Y-%m-%d')
|
||||
save_queryDataListItemNos_xls(data_df,dataItemNoList)
|
||||
print('当月数据更新完成')
|
||||
|
||||
|
||||
def save_xls_1(append_rows):
|
||||
|
||||
# 打开xls文件
|
||||
workbook = xlrd.open_workbook('沥青数据项.xlsx')
|
||||
|
||||
# 获取所有sheet的个数
|
||||
sheet_count = len(workbook.sheet_names())
|
||||
|
||||
# 获取所有sheet的名称
|
||||
sheet_names = workbook.sheet_names()
|
||||
|
||||
new_workbook = xlwt.Workbook()
|
||||
for i in range(sheet_count):
|
||||
# 获取当前sheet
|
||||
sheet = workbook.sheet_by_index(i)
|
||||
|
||||
# 获取sheet的行数和列数
|
||||
row_count = sheet.nrows - 1
|
||||
col_count = sheet.ncols
|
||||
# 获取原有数据
|
||||
data = []
|
||||
for row in range(row_count):
|
||||
row_data = []
|
||||
for col in range(col_count):
|
||||
row_data.append(sheet.cell_value(row, col))
|
||||
data.append(row_data)
|
||||
# 创建xlwt的Workbook对象
|
||||
# 创建sheet
|
||||
new_sheet = new_workbook.add_sheet(sheet_names[i])
|
||||
|
||||
# 将原有的数据写入新的sheet
|
||||
for row in range(row_count):
|
||||
for col in range(col_count):
|
||||
new_sheet.write(row, col, data[row][col])
|
||||
|
||||
if i == 0:
|
||||
# 在新的sheet中添加数据
|
||||
for col in range(col_count):
|
||||
new_sheet.write(row_count, col, append_rows[col])
|
||||
|
||||
# 保存新的xls文件
|
||||
new_workbook.save("沥青数据项.xlsx")
|
||||
|
||||
|
||||
|
||||
def start(date=''):
|
||||
"""获取当日数据"""
|
||||
read_xls_data()
|
||||
token = get_head_auth()
|
||||
if not token:
|
||||
return
|
||||
|
||||
cur_time,cur_time2 = getNow(date)
|
||||
print(f"获取{cur_time}数据")
|
||||
datas = get_data_value(token, one_cols,date=cur_time)
|
||||
print(len(datas))
|
||||
print(datas)
|
||||
if not datas:
|
||||
return
|
||||
|
||||
append_rows = [cur_time2]
|
||||
dataItemNo_dataValue = {}
|
||||
for data_value in datas:
|
||||
if "dataValue" not in data_value:
|
||||
print(data_value)
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = ""
|
||||
else:
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
|
||||
|
||||
for value in one_cols:
|
||||
if value in dataItemNo_dataValue:
|
||||
append_rows.append(dataItemNo_dataValue[value])
|
||||
else:
|
||||
append_rows.append("")
|
||||
|
||||
print('添加的行：',len(append_rows),append_rows)
|
||||
save_xls_2(append_rows)
|
||||
|
||||
|
||||
def save_xls_2(append_rows):
|
||||
"""保存或更新数据到Excel文件
|
||||
参数:
|
||||
append_rows (list): 需要追加/更新的数据行，格式为[日期, 数据项1, 数据项2,...]
|
||||
"""
|
||||
# try:
|
||||
# 读取现有数据（假设第一行为列名）
|
||||
df = pd.read_excel('沥青数据项.xlsx', sheet_name=0)
|
||||
print('文件中的数据列数：',len(df.columns),df.columns)
|
||||
# 转换append_rows为DataFrame
|
||||
if len(append_rows) != len(df.columns):
|
||||
# 去除第二个元素 ，不知道什么原因多一个空数据
|
||||
append_rows.pop(1)
|
||||
append_rows = pd.DataFrame([append_rows],columns=df.columns)
|
||||
# 创建新数据行
|
||||
new_date = append_rows['日期'].values[0]
|
||||
|
||||
dates = df['日期'].to_list()
|
||||
# 判断日期是否存在
|
||||
if new_date in dates:
|
||||
# 找到日期所在行的索引
|
||||
date_mask = df['日期'] == new_date
|
||||
# 存在则更新数据
|
||||
df.loc[date_mask] = append_rows.values
|
||||
print(f"更新 {new_date} 数据")
|
||||
else:
|
||||
# 不存在则追加数据
|
||||
df = pd.concat([df, append_rows], ignore_index=True)
|
||||
print(df.head())
|
||||
print(df.tail())
|
||||
print(f"插入 {new_date} 新数据")
|
||||
|
||||
# 保存更新后的数据
|
||||
df.to_excel('沥青数据项.xlsx', index=False, engine='openpyxl')
|
||||
|
||||
# except FileNotFoundError:
|
||||
# # 如果文件不存在则创建新文件
|
||||
# pd.DataFrame([append_rows]).to_excel('沥青数据项.xlsx', index=False, engine='openpyxl')
|
||||
# except Exception as e:
|
||||
# print(f"保存数据时发生错误: {str(e)}")
|
||||
|
||||
|
||||
def main(start_date=None,token=None,token_push=None):
|
||||
from datetime import datetime, timedelta
|
||||
if start_date is None:
|
||||
start_date = datetime.now()
|
||||
if token is None:
|
||||
token = get_head_auth()
|
||||
if token_push is None:
|
||||
token_push = get_head_push_auth()
|
||||
date = start_date.strftime('%Y%m%d')
|
||||
print(date)
|
||||
# start(date)
|
||||
# 更新当月数据
|
||||
queryDataListItemNos(token)
|
||||
# 训练模型
|
||||
optimize_Model()
|
||||
# # 预测&上传预测结果
|
||||
upload_data_to_system(token_push,start_date)
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("运行中ing...")
|
||||
main()
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@ -2,7 +2,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -606,6 +606,7 @@
|
||||
" return None\n",
|
||||
"\n",
|
||||
"def save_queryDataListItemNos_xls(data_df,dataItemNoList):\n",
|
||||
" from datetime import datetime, timedelta\n",
|
||||
" current_year_month = datetime.now().strftime('%Y-%m')\n",
|
||||
" grouped = data_df.groupby(\"dataDate\")\n",
|
||||
"\n",
|
||||
@ -667,7 +668,9 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def main(start_date=None,token=None,token_push=None):\n",
|
||||
" from datetime import datetime, timedelta\n",
|
||||
" if start_date is None:\n",
|
||||
" start_date = datetime.now()\n",
|
||||
" if token is None:\n",
|
||||
@ -676,10 +679,14 @@
|
||||
" token_push = get_head_push_auth()\n",
|
||||
" date = start_date.strftime('%Y%m%d')\n",
|
||||
" print(date)\n",
|
||||
" # 更新当月数据\n",
|
||||
" queryDataListItemNos(start_date,token)\n",
|
||||
" # 更新当日数据\n",
|
||||
"# start(date)\n",
|
||||
" try:\n",
|
||||
" # 更新当月数据\n",
|
||||
" queryDataListItemNos(start_date,token)\n",
|
||||
" except:\n",
|
||||
" print('当月数据更新失败,单日更新')\n",
|
||||
" start(date)\n",
|
||||
" # 更新当日数据，批量日期更新时打开\n",
|
||||
" # start(date)\n",
|
||||
" # 训练模型\n",
|
||||
" optimize_Model()\n",
|
||||
" # # 预测&上传预测结果\n",
|
||||
@ -707,7 +714,744 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"运行中ing...\n"
|
||||
"运行中ing...\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0NzkxMjg3NywianRpIjoiM2Y0YTVjZjMxYjQ5NGI4MmEyODNhYmQ0ODY0NDdlODgifQ.9Pz1IswwjZrcBAkGQaF2DS8toGjSWdW7XM2ewSEnunY\n",
|
||||
"20250522\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\IPython\\core\\magics\\pylab.py:160: UserWarning:\n",
|
||||
"\n",
|
||||
"pylab import has clobbered these variables: ['datetime', 'random', '__version__', 'plot']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Date\n",
|
||||
"2025-05-22 4686.071289\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4686.07\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0Nzk5ODk3NywianRpIjoiM2FiNWY0YTMxYmVkNGIwNDhkODQ5ZTcxNzE4NGIzZjAifQ.7u4XG8lVrdLo0v8HkFuh-39LUQlvKndx-eehgaHgqiQ\n",
|
||||
"20250523\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-23 4745.852051\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4745.85\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0Nzk5OTI3NywianRpIjoiZTVjMzMzYzdmMmYxNDRkNmJjOGUwMjM1NTE5Zjg1MDAifQ.k80aeQlgTPDdVxbmnB3RzjL2B2rvsDrphhJFRRVQ7hc\n",
|
||||
"20250523\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-23 4745.852051\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4745.85\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODA4NTM3OCwianRpIjoiMmE1NzA0NGE0OTM5NGFmODk5MGYzZGIxYTU2MjlkNjgifQ.smTWAknViqKT89RK6wfecUU6RVz-rk-lk__RMXEAd7U\n",
|
||||
"20250524\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-24 4733.198242\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4733.2\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODA4NTY3NywianRpIjoiOWQ3YTc5NDQ5MzRkNDNmNzllZmUxZmQxN2EzYmQ2ZTAifQ.p1kAb2XS4zXsX4L9LxYx_WARMXwGBsSi6-0fLgPHwI8\n",
|
||||
"20250524\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-24 4733.198242\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4733.2\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODE3MTc3OCwianRpIjoiYmJjZGRkODg5YTJmNDJhMzg0MDg2NjIxNWE4OTM2ZTQifQ.5kl8fb4i-GLc9gwkoAZg2sFYUJRoe2_DcbTPEIUvfKc\n",
|
||||
"20250525\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-24 4702.740723\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4702.74\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODE3MjA3OCwianRpIjoiYjcxYzhjNTBlOTE0NGIyZmFjNTViOGFiMGFkMjg4MDcifQ.BdY7aAgJLFALAb6ZEaxsUdXdZBD9kKL4cH2usKKoTuM\n",
|
||||
"20250525\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-24 4702.740723\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4702.74\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODI1ODE3OCwianRpIjoiMzEwYjk2OTg1YzY3NGJkMTliNWQ5Njg0MjJlZDEwMDIifQ.hWMl89A0qXItRw4ilOjF5c3q5zGYG4WkvFggkxPB4qI\n",
|
||||
"20250526\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-26 4657.152344\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4657.15\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODI1ODQ3OCwianRpIjoiNGE0YjY5OTlmYmUyNDNiYmJhNDc3NTU3ZjYzMDQyMWIifQ.Vfp9D9sHUC_Z-E7dwb-z-Saw-YgvIuEsyfCa1HAO3tA\n",
|
||||
"20250526\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-26 4657.152344\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4657.15\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODM0NDU3OCwianRpIjoiYTMxZDdmZWRlNzk2NDA1YTllMDM3NTU0ZTg2ZTEzOGYifQ.DeLFIsqo8NodaW9t98K08nHF_X_uV5dQgkuG7fwMBis\n",
|
||||
"20250527\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-27 4700.042969\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4700.04\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODM0NDg3OCwianRpIjoiZmE3NjFiNDE4N2EzNDAzODhjOTkzMDNjYmY2NmZkMzMifQ.0DqOj6VtXfRKPfTRrnWQawd2QqC5U_VdjUohWQxLkg4\n",
|
||||
"20250527\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-27 4700.042969\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4700.04\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODQzMDk3OSwianRpIjoiNWJjNjUxMTY3NWY1NDUzZWIwMzEzNTEwZjAzYThmZTIifQ.ZKJZ0MKm9o1ygfdf7K0OTrh5BKqXqxuHqSIyj-o_D5Q\n",
|
||||
"20250528\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-28 4684.207031\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4684.21\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODQzMTI3OSwianRpIjoiZWU4YjA2NWEzZTIyNDg3MWE1NGJhMTg4NTE4MTM0MDMifQ.tfZ8OEM_lBg20OaMQB-U9LzeLaDFTlRMHYhJyW2lWB8\n",
|
||||
"20250528\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-28 4684.207031\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4684.21\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODUxNzM3OSwianRpIjoiZDdmYjg3NDY4ZGRkNDYyNmFiYjE4YTdjZjdiZTkyN2UifQ.hz0SzEtdaGwgJHWf8XheBXYJSU06eFb5DbYAhnLYCmI\n",
|
||||
"20250529\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-29 4700.171875\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4700.17\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODUxNzY4MSwianRpIjoiZjVmM2JmMDE3ZjE2NDMzMTg0MDZjODZkNWY1Mzk1YTMifQ.-gvwjjIg5Bng8ceyZ8Jsb-zPQP9qW_cwATowtL6HYMQ\n",
|
||||
"20250529\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-29 4700.171875\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4700.17\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODYwMzc4MCwianRpIjoiYThlYTEzMWI0NzA5NDE4Y2E0NWIwZWQzMDNhZDAyZWMifQ.VtKVo59zkqa2xomzhigfqRsIIg7lD_tAQO8pgVt_Luc\n",
|
||||
"20250530\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-30 4651.634277\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4651.63\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODYwNDA4MCwianRpIjoiYzY2MWI4MWMyNDk4NDY0Y2JiZDkxZGZhOGMyZGUxNTcifQ.lS7jRPhQ4M1TQlsTTFEfR3f4cwLfH98h4qOmBrKgio0\n",
|
||||
"20250530\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-30 4651.634277\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4651.63\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODY5MDE4MCwianRpIjoiNjFjMGEwMTgxNTc2NDFlZjgxZDIxNTZkMGU1MjhiZDcifQ.snloJkEdEctvxKjuAnonz2yRFumN434ub4mg0jHTP1M\n",
|
||||
"20250531\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-31 4600.956543\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4600.96\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODY5MDQ4MCwianRpIjoiMWI2OGM5MzRhYWQxNGI2YTk5MGEwZGQyZDdjYjcwYzEifQ.3IW-0YoJg-kv9l0w4iHcsKAcFb2BL7EZIFDnBxtuIqM\n",
|
||||
"20250531\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-05-31 4600.956543\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4600.96\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODc3NjU4MSwianRpIjoiMTE5ODg4Zjc3NGEwNGE1ZDk1MmIyNWJiYzA1OTY2OWUifQ.SxQgQjsREFVyeiggyzc2kVHy_UAH_z9saIXUkX9JXoM\n",
|
||||
"20250601\n",
|
||||
"09:15:00执行失败\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODc3Njg4MCwianRpIjoiOTk0NTRmMDcwMTdiNGIzM2JjMzA5ZWM5NjNiOWY1MjAifQ.8ir_vmh4R9AD-hAL1RjBXwVtPcuzhMLK0Yxf1c0ugXI\n",
|
||||
"20250601\n",
|
||||
"09:20:00执行失败\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODc3Njg4MCwianRpIjoiM2QzOGQ3Y2MzYzhlNGRjNWI4MjliMWM3ZGU1NzNiNjYifQ.EsDazleLcFiWS0cummkTv_0w8Eba0Q1o0tzRG82DjuQ\n",
|
||||
"20250601\n",
|
||||
"09:20:00执行失败\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODc3Njg4MSwianRpIjoiY2I0NjE2NDUxM2QxNGNkNjg5ZWY1YmNmZDFmNTMyMjQifQ.MdZEinDQ8g0dS-kz9Ffjr-a_THlssHY2tUG3afSFvF4\n",
|
||||
"20250601\n",
|
||||
"09:20:00执行失败\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODc3Njg4MSwianRpIjoiNzM5MWZiNzdlMmJkNDA1N2FlNGE1MzQ4NTc0NjA4NGYifQ.eFqVfiQFOg45CSJD1liw7-bBrGQu-Bvmh3K4x2WTb3o\n",
|
||||
"20250601\n",
|
||||
"09:20:00执行失败\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODg2Mjk4MSwianRpIjoiYmVlZWQwZWZjMzEyNDA2Yjg0NTAzZTMxMTNiMDA1OWYifQ.TmWrq7FOObj2HpzV-xLDmwuj1al9n6mgk_fljxJx22Q\n",
|
||||
"20250602\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-06-02 4594.924316\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4594.92\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODg2MzI4MSwianRpIjoiMzE5NzcxNDMyZmQ5NGE0MWIwNTYzMzY2ZjQ4NTgxMzUifQ.vnVbDAkT75p6MIUZb6yDC7s8yHuDO6HWL0bW735D8Zg\n",
|
||||
"20250602\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-06-02 4594.924316\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4594.92\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODk0OTM4MiwianRpIjoiZjkwZmZmMGQwNzJlNDc4NmE2ODBkYjkxYWU1OGFjOTkifQ.ZxzSHX9AuEx0F8_ZTK9UN_kKZoC2KC_fmaW333JlGwQ\n",
|
||||
"20250603\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-06-03 4627.078125\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4627.08\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0ODk0OTY4MywianRpIjoiYTBkNmU3YTU5NmRiNDBjYzk1NmFiOTkwZTY5ZjYyZWIifQ.vAXcKM8JkBMXTcTQ6EONidFybyVdNTz2ClxFEYJZBEo\n",
|
||||
"20250603\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-06-03 4627.078125\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4627.08\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0OTAzNTc4MiwianRpIjoiMjZjZjFmZTMzYzE1NDIxZWE3M2M5ZDk3NWZhMzZjYmUifQ.MI_Zs0NN4n4IqMxzP_JK-2EUPjZDBT-hUepkXOzAVR4\n",
|
||||
"20250604\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-06-04 4696.86084\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4696.86\n",
|
||||
"执行定时任务\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0OTAzNjA4MiwianRpIjoiM2RiZjNjNTYwYmM0NGM3ZDgzYmViZTBlYTQ5MGVmMjkifQ.MuXgas16l6swMv0KVRsWT9pBwFYWGroR9D38lx4A5mI\n",
|
||||
"20250604\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:603: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: Qt5Agg\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n",
|
||||
"Date\n",
|
||||
"2025-06-04 4696.86084\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4696.86\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -730,7 +1474,8 @@
|
||||
" time.sleep(1)\n",
|
||||
" except:\n",
|
||||
" print(f\"{current_time}执行失败\")\n",
|
||||
"# main() \n",
|
||||
"# main() \n",
|
||||
"# main() \n",
|
||||
" # 检测数据准确性, 需要检测放开\n",
|
||||
" # check_data(\"100028098|LISTING_PRICE\")\n",
|
||||
" # check_data(\"9137070016544622XB|DAY_Yield\")\n"
|
||||
@ -771,18 +1516,6 @@
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Binary file not shown.
663
aisenzhecode/液化石油气/液化气每日价格预测.py
Normal file
663
aisenzhecode/液化石油气/液化气每日价格预测.py
Normal file
@ -0,0 +1,663 @@
|
||||
import requests
|
||||
import json
|
||||
|
||||
from datetime import datetime,timedelta
|
||||
|
||||
# 变量定义
|
||||
login_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
search_url = "http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos"
|
||||
queryDataListItemNos_url = "http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
|
||||
|
||||
login_push_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
upload_url = "http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
login_push_data = {
|
||||
"data": {
|
||||
"account": "api_dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
read_file_path_name = "液化气数据.xlsx"
|
||||
one_cols = []
|
||||
two_cols = []
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sn
|
||||
import random
|
||||
import time
|
||||
|
||||
from plotly import __version__
|
||||
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
|
||||
|
||||
from sklearn import preprocessing
|
||||
|
||||
from pandas import Series,DataFrame
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import sklearn.datasets as datasets
|
||||
|
||||
#导入机器学习算法模型
|
||||
from sklearn.linear_model import Lasso
|
||||
from xgboost import XGBRegressor
|
||||
|
||||
import statsmodels.api as sm
|
||||
try:
|
||||
from keras.preprocessing.sequence import TimeseriesGenerator
|
||||
except:
|
||||
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
|
||||
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
|
||||
import xgboost as xgb
|
||||
from xgboost import plot_importance, plot_tree
|
||||
from sklearn.metrics import mean_absolute_error
|
||||
from statsmodels.tools.eval_measures import mse,rmse
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from xgboost import XGBRegressor
|
||||
import warnings
|
||||
import pickle
|
||||
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
#切割训练数据和样本数据
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
#用于模型评分
|
||||
from sklearn.metrics import r2_score
|
||||
|
||||
le = preprocessing.LabelEncoder()
|
||||
|
||||
# print(__version__) # requires version >= 1.9.0
|
||||
|
||||
|
||||
import cufflinks as cf
|
||||
cf.go_offline()
|
||||
|
||||
random.seed(100)
|
||||
|
||||
|
||||
# 数据获取
|
||||
|
||||
def get_head_auth():
|
||||
login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))
|
||||
text = json.loads(login_res.text)
|
||||
if text["status"]:
|
||||
token = text["data"]["accessToken"]
|
||||
print('获取的token:',token)
|
||||
return token
|
||||
else:
|
||||
print("获取认证失败")
|
||||
return None
|
||||
|
||||
|
||||
def get_data_value(token, dataItemNoList,date):
|
||||
search_data = {
|
||||
"data": {
|
||||
"date": date,
|
||||
"dataItemNoList": dataItemNoList
|
||||
},
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询"
|
||||
}
|
||||
|
||||
|
||||
headers = {"Authorization": token}
|
||||
search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))
|
||||
print('数据项查询参数search_data：')
|
||||
print(search_data)
|
||||
print('数据项查询结果search_res：')
|
||||
print(search_res.text)
|
||||
|
||||
try:
|
||||
search_value = json.loads(search_res.text)["data"]
|
||||
|
||||
print("数据项查询结果:", search_value)
|
||||
except json.JSONDecodeError as e:
|
||||
print(f"Error decoding JSON: {e}")
|
||||
print("Response content:", search_res.text)
|
||||
return None
|
||||
if search_value:
|
||||
return search_value
|
||||
else:
|
||||
print("今天没有新数据")
|
||||
return search_value
|
||||
|
||||
|
||||
def get_head_push_auth():
|
||||
login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))
|
||||
text = json.loads(login_res.text)
|
||||
if text["status"]:
|
||||
token = text["data"]["accessToken"]
|
||||
return token
|
||||
else:
|
||||
print("获取认证失败")
|
||||
return None
|
||||
|
||||
|
||||
|
||||
def upload_data_to_system(token_push,date):
|
||||
data = {
|
||||
"funcModule": "数据表信息列表",
|
||||
"funcOperation": "新增",
|
||||
"data": [
|
||||
{"dataItemNo": "250855713|Forecast_Price|ACN",
|
||||
"dataDate": getNow(date=date)[0],
|
||||
"dataStatus": "add",
|
||||
"dataValue": forecast_price()
|
||||
}
|
||||
|
||||
]
|
||||
}
|
||||
headers = {"Authorization": token_push}
|
||||
res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))
|
||||
print(res.text)
|
||||
print('预测值：',data['data'][0]['dataValue'])
|
||||
|
||||
|
||||
price_list = []
|
||||
|
||||
def forecast_price():
|
||||
# df_test = pd.read_csv('定价模型数据收集0212.csv')
|
||||
df_test = pd.read_excel('液化气数据.xlsx')
|
||||
df_test.drop([0],inplace=True)
|
||||
try:
|
||||
df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)
|
||||
except:
|
||||
df_test['Date']=pd.to_datetime(df_test['Date'], format=r'%Y-%m-%d',infer_datetime_format=True)
|
||||
|
||||
|
||||
df_test_1 = df_test
|
||||
df_test_1=df_test_1.fillna(df_test.ffill())
|
||||
df_test_1=df_test_1.fillna(df_test_1.bfill())
|
||||
|
||||
# 选择用于模型训练的列名称
|
||||
col_for_training = df_test_1.columns
|
||||
|
||||
|
||||
|
||||
|
||||
import joblib
|
||||
Best_model_DalyLGPrice = joblib.load("日度价格预测_液化气最佳模型.pkl")
|
||||
# 最新的一天为最后一行的数据
|
||||
|
||||
df_test_1_Day = df_test_1.tail(1)
|
||||
# 移除不需要的列
|
||||
df_test_1_Day.index = df_test_1_Day["Date"]
|
||||
df_test_1_Day = df_test_1_Day.drop(["Date"], axis= 1)
|
||||
df_test_1_Day=df_test_1_Day.drop('Price',axis=1)
|
||||
df_test_1_Day=df_test_1_Day.dropna()
|
||||
|
||||
for col in df_test_1_Day.columns:
|
||||
df_test_1_Day[col] = pd.to_numeric(df_test_1_Day[col],errors='coerce')
|
||||
#预测今日价格，显示至小数点后两位
|
||||
Ypredict_Today=Best_model_DalyLGPrice.predict(df_test_1_Day)
|
||||
|
||||
df_test_1_Day['日度预测价格']=Ypredict_Today
|
||||
print(df_test_1_Day['日度预测价格'])
|
||||
a = df_test_1_Day['日度预测价格']
|
||||
a = a[0]
|
||||
a = float(a)
|
||||
a = round(a,2)
|
||||
price_list.append(a)
|
||||
return a
|
||||
def optimize_Model():
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.impute import SimpleImputer
|
||||
from sklearn.preprocessing import OrdinalEncoder
|
||||
from sklearn.feature_selection import SelectFromModel
|
||||
from sklearn.metrics import mean_squared_error, r2_score
|
||||
import pandas as pd
|
||||
|
||||
pd.set_option('display.max_rows',40)
|
||||
pd.set_option('display.max_columns',40)
|
||||
df_test = pd.read_excel('液化气数据.xlsx')
|
||||
df_test.drop([0],inplace=True)
|
||||
try:
|
||||
df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)
|
||||
except:
|
||||
df_test['Date']=pd.to_datetime(df_test['Date'], format=r'%Y-%m-%d',infer_datetime_format=True)
|
||||
|
||||
|
||||
#将缺失值补为前一个或者后一个数值
|
||||
df_test_1 = df_test
|
||||
df_test_1=df_test_1.fillna(df_test.ffill())
|
||||
df_test_1=df_test_1.fillna(df_test_1.bfill())
|
||||
df_test_1["Date"] = pd.to_datetime(df_test_1["Date"])
|
||||
df_test_1.index = df_test_1["Date"]
|
||||
df_test_1 = df_test_1.drop(["Date"], axis= 1)
|
||||
df_test_1 = df_test_1.astype('float')
|
||||
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from pandas import Series,DataFrame
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import sklearn.datasets as datasets
|
||||
|
||||
#导入机器学习算法模型
|
||||
from sklearn.linear_model import Lasso
|
||||
from xgboost import XGBRegressor
|
||||
|
||||
import statsmodels.api as sm
|
||||
try:
|
||||
from keras.preprocessing.sequence import TimeseriesGenerator
|
||||
except:
|
||||
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
|
||||
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
|
||||
import xgboost as xgb
|
||||
from xgboost import plot_importance, plot_tree
|
||||
from sklearn.metrics import mean_absolute_error
|
||||
from statsmodels.tools.eval_measures import mse,rmse
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from xgboost import XGBRegressor
|
||||
import warnings
|
||||
import pickle
|
||||
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
#切割训练数据和样本数据
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
#用于模型评分
|
||||
from sklearn.metrics import r2_score
|
||||
|
||||
dataset1=df_test_1.drop('Price',axis=1)#.astype(float)
|
||||
|
||||
y=df_test_1['Price']
|
||||
|
||||
x=dataset1
|
||||
|
||||
train = x
|
||||
target = y
|
||||
|
||||
#切割数据样本集合测试集
|
||||
X_train,x_test,y_train,y_true = train_test_split(train,target,test_size=0.2,random_state=0)
|
||||
|
||||
#模型缩写
|
||||
Lasso = Lasso(random_state=0)
|
||||
XGBR = XGBRegressor(random_state=0)
|
||||
#训练模型
|
||||
Lasso.fit(X_train,y_train)
|
||||
XGBR.fit(X_train,y_train)
|
||||
#模型拟合
|
||||
y_pre_Lasso = Lasso.predict(x_test)
|
||||
y_pre_XGBR = XGBR.predict(x_test)
|
||||
|
||||
#计算Lasso、XGBR、RandomForestR、AdaBoostR、GradientBoostingR、BaggingRegressor各模型的R²
|
||||
Lasso_score = r2_score(y_true,y_pre_Lasso)
|
||||
XGBR_score=r2_score(y_true,y_pre_XGBR)
|
||||
|
||||
#计算Lasso、XGBR的MSE和RMSE
|
||||
Lasso_MSE=mean_squared_error(y_true, y_pre_Lasso)
|
||||
XGBR_MSE=mean_squared_error(y_true, y_pre_XGBR)
|
||||
|
||||
Lasso_RMSE=np.sqrt(Lasso_MSE)
|
||||
XGBR_RMSE=np.sqrt(XGBR_MSE)
|
||||
# 将不同模型的不同误差值整合成一个表格
|
||||
model_results = pd.DataFrame([['Lasso', Lasso_RMSE, Lasso_score],
|
||||
['XgBoost', XGBR_RMSE, XGBR_score]],
|
||||
columns = ['模型(Model)','均方根误差(RMSE)', 'R^2 score'])
|
||||
#将模型名称(Model)列设置为索引
|
||||
model_results1=model_results.set_index('模型(Model)')
|
||||
|
||||
model_results1
|
||||
#定义plot_feature_importance函数，该函数用于计算特征重要性。此部分代码无需调整
|
||||
def plot_feature_importance(importance,names,model_type):
|
||||
feature_importance = np.array(importance)
|
||||
feature_names = np.array(names)
|
||||
|
||||
data={'feature_names':feature_names,'feature_importance':feature_importance}
|
||||
fi_df = pd.DataFrame(data)
|
||||
|
||||
fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)
|
||||
|
||||
plt.figure(figsize=(10,8))
|
||||
sn.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])
|
||||
|
||||
plt.title(model_type + " "+'FEATURE IMPORTANCE')
|
||||
plt.xlabel('FEATURE IMPORTANCE')
|
||||
plt.ylabel('FEATURE NAMES')
|
||||
from pylab import mpl
|
||||
mpl.rcParams['font.sans-serif'] = ['SimHei']
|
||||
## Xgboost 模型参数优化-初步
|
||||
#参考: https://juejin.im/post/6844903661013827598
|
||||
#每次调参时，备选参数数值以同数量级的1、3、10设置即可（比如设置1、3、10，或0.1、0.3、1.0，或0.01,0.03,0.10即可）
|
||||
|
||||
from xgboost import XGBRegressor
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
|
||||
estimator = XGBRegressor(random_state=0,
|
||||
nthread=4,
|
||||
seed=0
|
||||
)
|
||||
parameters = {
|
||||
'max_depth': range (2, 11, 2), # 树的最大深度
|
||||
'n_estimators': range (50, 101, 10), # 迭代次数
|
||||
'learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]
|
||||
}
|
||||
|
||||
grid_search_XGB = GridSearchCV(
|
||||
estimator=estimator,
|
||||
param_grid=parameters,
|
||||
# n_jobs = 10,
|
||||
cv = 3,
|
||||
verbose=True
|
||||
)
|
||||
|
||||
grid_search_XGB.fit(X_train, y_train)
|
||||
#如果电脑在此步骤报错，可能是因为计算量太大，超过硬件可支持程度，可注释掉“n_jobs=10”一行
|
||||
|
||||
best_parameters = grid_search_XGB.best_estimator_.get_params()
|
||||
y_pred = grid_search_XGB.predict(x_test)
|
||||
|
||||
op_XGBR_score = r2_score(y_true,y_pred)
|
||||
op_XGBR_MSE= mean_squared_error(y_true, y_pred)
|
||||
op_XGBR_RMSE= np.sqrt(op_XGBR_MSE)
|
||||
|
||||
model_results2 = pd.DataFrame([['Optimized_Xgboost', op_XGBR_RMSE, op_XGBR_score]],
|
||||
columns = ['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])
|
||||
model_results2=model_results2.set_index('模型(Model)')
|
||||
|
||||
try:
|
||||
results = model_results1.append(model_results2, ignore_index = False)
|
||||
except:
|
||||
results = pd.concat([model_results1,model_results2],ignore_index=True)
|
||||
import pickle
|
||||
|
||||
Pkl_Filename = "日度价格预测_液化气最佳模型.pkl"
|
||||
|
||||
with open(Pkl_Filename, 'wb') as file:
|
||||
pickle.dump(grid_search_XGB, file)
|
||||
|
||||
def read_xls_data():
|
||||
"""获取特征项ID"""
|
||||
global one_cols, two_cols
|
||||
# 使用pandas读取Excel文件
|
||||
df = pd.read_excel(read_file_path_name, header=None) # 不自动识别列名
|
||||
# 获取第二行数据（索引为1）
|
||||
one_cols = df.iloc[1].tolist()[1:]
|
||||
print(f'获取到的数据项ID{one_cols}')
|
||||
|
||||
|
||||
def start(date=''):
|
||||
"""获取当日数据"""
|
||||
read_xls_data()
|
||||
token = get_head_auth()
|
||||
if not token:
|
||||
return
|
||||
|
||||
cur_time,cur_time2 = getNow(date)
|
||||
print(f"获取{cur_time}数据")
|
||||
datas = get_data_value(token, one_cols,date=cur_time)
|
||||
if not datas:
|
||||
return
|
||||
|
||||
append_rows = [cur_time2]
|
||||
dataItemNo_dataValue = {}
|
||||
for data_value in datas:
|
||||
if "dataValue" not in data_value:
|
||||
print(data_value)
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = ""
|
||||
else:
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
|
||||
|
||||
for value in one_cols:
|
||||
if value in dataItemNo_dataValue:
|
||||
append_rows.append(dataItemNo_dataValue[value])
|
||||
else:
|
||||
append_rows.append("")
|
||||
print('添加的行：',append_rows)
|
||||
save_xls_2(append_rows)
|
||||
|
||||
|
||||
def getNow(date='', offset=0):
|
||||
"""生成指定日期的两种格式字符串
|
||||
Args:
|
||||
date: 支持多种输入类型：
|
||||
- datetime对象
|
||||
- 字符串格式（支持'%Y-%m-%d'和'%Y%m%d'）
|
||||
- 空字符串表示当前日期
|
||||
offset: 日期偏移天数
|
||||
Returns:
|
||||
tuple: (紧凑日期字符串, 标准日期字符串)
|
||||
"""
|
||||
# 日期解析逻辑
|
||||
from datetime import datetime,timedelta
|
||||
if isinstance(date, datetime):
|
||||
now = date
|
||||
else:
|
||||
now = datetime.now()
|
||||
if date:
|
||||
# 尝试多种日期格式解析
|
||||
for fmt in ('%Y-%m-%d', '%Y%m%d', '%Y/%m/%d'):
|
||||
try:
|
||||
now = datetime.strptime(str(date), fmt)
|
||||
break
|
||||
except ValueError:
|
||||
continue
|
||||
else:
|
||||
raise ValueError(f"无法解析的日期格式: {date}")
|
||||
|
||||
# 应用日期偏移
|
||||
now = now - timedelta(days=offset)
|
||||
|
||||
# 统一格式化输出
|
||||
date_str = now.strftime("%Y-%m-%d")
|
||||
compact_date = date_str.replace("-", "")
|
||||
return compact_date, date_str
|
||||
|
||||
def start_1(date=''):
|
||||
"""补充昨日数据"""
|
||||
read_xls_data()
|
||||
token = get_head_auth()
|
||||
if not token:
|
||||
return
|
||||
|
||||
cur_time,cur_time2 = getNow(date,offset=1)
|
||||
print(f"补充{cur_time}数据")
|
||||
datas = get_data_value(token, one_cols,date=cur_time)
|
||||
if not datas:
|
||||
print(f"{cur_time}没有数据")
|
||||
return
|
||||
|
||||
append_rows = [cur_time2]
|
||||
dataItemNo_dataValue = {}
|
||||
for data_value in datas:
|
||||
if "dataValue" not in data_value:
|
||||
print(data_value)
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = ""
|
||||
else:
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
|
||||
|
||||
for value in one_cols:
|
||||
if value in dataItemNo_dataValue:
|
||||
append_rows.append(dataItemNo_dataValue[value])
|
||||
else:
|
||||
append_rows.append("")
|
||||
print('添加的行：',append_rows)
|
||||
save_xls_2(append_rows)
|
||||
|
||||
|
||||
def save_xls_2(append_rows):
|
||||
"""保存或更新数据到Excel文件
|
||||
参数:
|
||||
append_rows (list): 需要追加/更新的数据行，格式为[日期, 数据项1, 数据项2,...]
|
||||
"""
|
||||
try:
|
||||
# 读取现有数据（假设第一行为列名）
|
||||
df = pd.read_excel('液化气数据.xlsx', sheet_name=0)
|
||||
# 转换append_rows为DataFrame
|
||||
append_rows = pd.DataFrame([append_rows],columns=df.columns)
|
||||
# 创建新数据行
|
||||
new_date = append_rows['Date'].values[0]
|
||||
|
||||
dates = df['Date'].to_list()
|
||||
# 判断日期是否存在
|
||||
if new_date in dates:
|
||||
# 找到日期所在行的索引
|
||||
date_mask = df['Date'] == new_date
|
||||
# 存在则更新数据
|
||||
df.loc[date_mask] = append_rows.values
|
||||
print(f"更新 {new_date} 数据")
|
||||
else:
|
||||
# 不存在则追加数据
|
||||
df = pd.concat([df, append_rows], ignore_index=True)
|
||||
print(df.head())
|
||||
print(df.tail())
|
||||
print(f"插入 {new_date} 新数据")
|
||||
|
||||
# 保存更新后的数据
|
||||
df.to_excel('液化气数据.xlsx', index=False, engine='openpyxl')
|
||||
|
||||
except FileNotFoundError:
|
||||
# 如果文件不存在则创建新文件
|
||||
pd.DataFrame([append_rows]).to_excel('液化气数据.xlsx', index=False, engine='openpyxl')
|
||||
except Exception as e:
|
||||
print(f"保存数据时发生错误: {str(e)}")
|
||||
|
||||
def check_data(dataItemNo):
|
||||
token = get_head_auth()
|
||||
if not token:
|
||||
return
|
||||
|
||||
datas = get_data_value(token, dataItemNo)
|
||||
if not datas:
|
||||
return
|
||||
|
||||
def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd):
|
||||
|
||||
search_data = {
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询",
|
||||
"data": {
|
||||
"dateStart": dateStart,
|
||||
"dateEnd": dateEnd,
|
||||
"dataItemNoList": dataItemNoList # 数据项编码,代表 brent最低价和最高价
|
||||
}
|
||||
}
|
||||
|
||||
headers = {"Authorization": token}
|
||||
search_res = requests.post(url=url, headers=headers, json=search_data, timeout=(3, 5))
|
||||
search_value = json.loads(search_res.text)["data"]
|
||||
if search_value:
|
||||
return search_value
|
||||
else:
|
||||
return None
|
||||
|
||||
def save_queryDataListItemNos_xls(data_df,dataItemNoList):
|
||||
from datetime import datetime, timedelta
|
||||
current_year_month = datetime.now().strftime('%Y-%m')
|
||||
grouped = data_df.groupby("dataDate")
|
||||
|
||||
# 使用openpyxl打开xlsx文件
|
||||
from openpyxl import load_workbook
|
||||
workbook = load_workbook('液化气数据.xlsx')
|
||||
|
||||
# 创建新工作簿
|
||||
new_workbook = load_workbook('液化气数据.xlsx')
|
||||
|
||||
for sheetname in workbook.sheetnames:
|
||||
sheet = workbook[sheetname]
|
||||
new_sheet = new_workbook[sheetname]
|
||||
|
||||
current_year_month_row = 0
|
||||
# 查找当前月份数据起始行
|
||||
for row_idx, row in enumerate(sheet.iter_rows(values_only=True), 1):
|
||||
if str(row[0]).startswith(current_year_month):
|
||||
current_year_month_row += 1
|
||||
|
||||
# 追加新数据
|
||||
if sheetname == workbook.sheetnames[0]:
|
||||
start_row = sheet.max_row - current_year_month_row + 1
|
||||
for row_idx, (date, group) in enumerate(grouped, start=start_row):
|
||||
new_sheet.cell(row=row_idx, column=1, value=date)
|
||||
for j, dataItemNo in enumerate(dataItemNoList, start=2):
|
||||
if group[group["dataItemNo"] == dataItemNo]["dataValue"].values:
|
||||
new_sheet.cell(row=row_idx, column=j,
|
||||
value=group[group["dataItemNo"] == dataItemNo]["dataValue"].values[0])
|
||||
|
||||
# 保存修改后的xlsx文件
|
||||
new_workbook.save("液化气数据.xlsx")
|
||||
|
||||
|
||||
def queryDataListItemNos(date=None,token=None):
|
||||
df = pd.read_excel('液化气数据.xlsx')
|
||||
dataItemNoList = df.iloc[0].tolist()[1:]
|
||||
if token is None:
|
||||
token = get_head_auth()
|
||||
if not token:
|
||||
print('token获取失败')
|
||||
return
|
||||
# 获取当前日期
|
||||
if date is None:
|
||||
current_date = datetime.now()
|
||||
else:
|
||||
current_date = date
|
||||
# 获取当月1日
|
||||
first_day_of_month = current_date.replace(day=1)
|
||||
# 格式化为 YYYYMMDD 格式
|
||||
dateEnd = current_date.strftime('%Y%m%d')
|
||||
dateStart = first_day_of_month.strftime('%Y%m%d')
|
||||
search_value = get_queryDataListItemNos_value(token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd)
|
||||
data_df = pd.DataFrame(search_value)
|
||||
data_df["dataDate"] = pd.to_datetime(data_df["dataDate"])
|
||||
data_df["dataDate"] = data_df["dataDate"].dt.strftime('%Y-%m-%d')
|
||||
save_queryDataListItemNos_xls(data_df,dataItemNoList)
|
||||
print('当月数据更新完成')
|
||||
|
||||
|
||||
|
||||
|
||||
def main(start_date=None,token=None,token_push=None):
|
||||
from datetime import datetime, timedelta
|
||||
if start_date is None:
|
||||
start_date = datetime.now()
|
||||
if token is None:
|
||||
token = get_head_auth()
|
||||
if token_push is None:
|
||||
token_push = get_head_push_auth()
|
||||
date = start_date.strftime('%Y%m%d')
|
||||
print(date)
|
||||
try:
|
||||
# 更新当月数据
|
||||
queryDataListItemNos(start_date,token)
|
||||
except:
|
||||
print('当月数据更新失败,单日更新')
|
||||
start(date)
|
||||
# 更新当日数据，批量日期更新时打开
|
||||
# start(date)
|
||||
# 训练模型
|
||||
optimize_Model()
|
||||
# # 预测&上传预测结果
|
||||
upload_data_to_system(token_push,start_date)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("运行中ing...")
|
||||
main()
|
||||
Binary file not shown.
@ -12,31 +12,14 @@
|
||||
"text/html": [
|
||||
" <script type=\"text/javascript\">\n",
|
||||
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
|
||||
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
|
||||
" if (typeof require !== 'undefined') {\n",
|
||||
" require.undef(\"plotly\");\n",
|
||||
" requirejs.config({\n",
|
||||
" paths: {\n",
|
||||
" 'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
|
||||
" }\n",
|
||||
" });\n",
|
||||
" require(['plotly'], function(Plotly) {\n",
|
||||
" window._Plotly = Plotly;\n",
|
||||
" });\n",
|
||||
" }\n",
|
||||
" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
|
||||
" </script>\n",
|
||||
" <script type=\"module\">import \"https://cdn.plot.ly/plotly-3.0.0.min\"</script>\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"运行中ing\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@ -865,12 +848,97 @@
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"20250601\n",
|
||||
"获取到的数据项ID['数据项编码', 'C01100047|STRIKE_PRICE', 'Brentspj', '913716251671540959|EXCHANGE_RATE', 'C01100010|LIST_PRICE01', '250326561|STRIKE_PRICE', 'C01100047|LIST_PRICE', 'C01100047|LIST_PRICE-1', 'C01100047|LIST_PRICE-01', 'OIL_CHEM|guonei|6097|PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370783724809024G|BEN|PRICE', '91370500737223620X|BEN|PRICE', '91370503706169019D|BEN|PRICE', '91370503164840647R|BEN|PRICE', 'C01100047|TURNOVER', '913705221649223519|C01100047|EXW', 'C01100047|CAPACITY']\n",
|
||||
"获取到的数据项ID['C01100047|STRIKE_PRICE', 'Brentspj', '913716251671540959|EXCHANGE_RATE', 'C01100010|LIST_PRICE01', '250326561|STRIKE_PRICE', 'C01100047|LIST_PRICE', 'C01100047|LIST_PRICE-1', 'C01100047|LIST_PRICE-01', 'OIL_CHEM|guonei|6097|PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370783724809024G|BEN|PRICE', '91370500737223620X|BEN|PRICE', '91370503706169019D|BEN|PRICE', '91370503164840647R|BEN|PRICE', 'C01100047|TURNOVER', '913705221649223519|C01100047|EXW', 'C01100047|CAPACITY']\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20756\\2211479266.py:753: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n",
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20756\\2211479266.py:302: UserWarning:\n",
|
||||
"\n",
|
||||
"The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
|
||||
"\n",
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20756\\2211479266.py:307: FutureWarning:\n",
|
||||
"\n",
|
||||
"Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
|
||||
"\n",
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20756\\2211479266.py:308: FutureWarning:\n",
|
||||
"\n",
|
||||
"Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
|
||||
"\n",
|
||||
"c:\\Users\\EDY\\.conda\\envs\\predict\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning:\n",
|
||||
"\n",
|
||||
"Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.936e+05, tolerance: 5.806e+04\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n",
|
||||
"Using matplotlib backend: inline\n",
|
||||
"%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"c:\\Users\\EDY\\.conda\\envs\\predict\\lib\\site-packages\\IPython\\core\\magics\\pylab.py:166: UserWarning:\n",
|
||||
"\n",
|
||||
"pylab import has clobbered these variables: ['datetime', '__version__', 'random', 'plot']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n",
|
||||
"\n",
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20756\\2211479266.py:255: UserWarning:\n",
|
||||
"\n",
|
||||
"The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
|
||||
"\n",
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20756\\2211479266.py:259: FutureWarning:\n",
|
||||
"\n",
|
||||
"Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
|
||||
"\n",
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20756\\2211479266.py:260: FutureWarning:\n",
|
||||
"\n",
|
||||
"Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
|
||||
"\n",
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_20756\\2211479266.py:285: FutureWarning:\n",
|
||||
"\n",
|
||||
"Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Date\n",
|
||||
"2025-06-04 5826.140137\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250601', 'dataStatus': 'add', 'dataValue': np.float64(5826.14)}]}\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# # # 自定义日期执行预测\n",
|
||||
"\n",
|
||||
"# start_date = datetime(2025, 5, 16)\n",
|
||||
"# end_date = datetime(2025, 5, 19)\n",
|
||||
"# start_date = datetime(2025, 6, 1)\n",
|
||||
"# end_date = datetime(2025, 6, 2)\n",
|
||||
"\n",
|
||||
"# token = get_head_auth()\n",
|
||||
"# token_push = get_head_push_auth()\n",
|
||||
@ -892,7 +960,7 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "predict",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -906,7 +974,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
"version": "3.10.16"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Binary file not shown.
794
aisenzhecode/石油苯/纯苯每日价格预测.py
Normal file
794
aisenzhecode/石油苯/纯苯每日价格预测.py
Normal file
@ -0,0 +1,794 @@
|
||||
import requests
|
||||
import json
|
||||
import xlrd
|
||||
import xlwt
|
||||
from datetime import datetime,timedelta
|
||||
import time
|
||||
# 变量定义
|
||||
login_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
search_url = "http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos"
|
||||
|
||||
login_push_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
upload_url = "http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
queryDataListItemNos_url = "http://10.200.32.39/jingbo-api//api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
login_push_data = {
|
||||
"data": {
|
||||
"account": "api_dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
read_file_path_name = "纯苯数据项.xls"
|
||||
one_cols = []
|
||||
two_cols = []
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sn
|
||||
import random
|
||||
import time
|
||||
|
||||
|
||||
|
||||
|
||||
from plotly import __version__
|
||||
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
|
||||
|
||||
from sklearn import preprocessing
|
||||
|
||||
from pandas import Series,DataFrame
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import sklearn.datasets as datasets
|
||||
|
||||
#导入机器学习算法模型
|
||||
from sklearn.linear_model import Lasso
|
||||
from xgboost import XGBRegressor
|
||||
|
||||
import statsmodels.api as sm
|
||||
try:
|
||||
from keras.preprocessing.sequence import TimeseriesGenerator
|
||||
except:
|
||||
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
|
||||
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
|
||||
import xgboost as xgb
|
||||
from xgboost import plot_importance, plot_tree
|
||||
from sklearn.metrics import mean_absolute_error
|
||||
from statsmodels.tools.eval_measures import mse,rmse
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from xgboost import XGBRegressor
|
||||
import warnings
|
||||
import pickle
|
||||
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
#切割训练数据和样本数据
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
#用于模型评分
|
||||
from sklearn.metrics import r2_score
|
||||
|
||||
le = preprocessing.LabelEncoder()
|
||||
|
||||
# print(__version__) # requires version >= 1.9.0
|
||||
|
||||
|
||||
import cufflinks as cf
|
||||
cf.go_offline()
|
||||
|
||||
random.seed(100)
|
||||
|
||||
|
||||
|
||||
# 数据获取
|
||||
|
||||
def get_head_auth():
|
||||
login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))
|
||||
text = json.loads(login_res.text)
|
||||
if text["status"]:
|
||||
token = text["data"]["accessToken"]
|
||||
return token
|
||||
else:
|
||||
print("获取认证失败")
|
||||
return None
|
||||
|
||||
|
||||
def get_data_value(token, dataItemNoList,date):
|
||||
search_data = {
|
||||
"data": {
|
||||
"date": getNow(date)[0],
|
||||
"dataItemNoList": dataItemNoList
|
||||
},
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询"
|
||||
}
|
||||
headers = {"Authorization": token}
|
||||
search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))
|
||||
search_value = json.loads(search_res.text)["data"]
|
||||
if search_value:
|
||||
return search_value
|
||||
else:
|
||||
print("今天没有新数据")
|
||||
return search_value
|
||||
|
||||
|
||||
# xls文件处理
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def write_xls(data,date):
|
||||
# 创建一个Workbook对象
|
||||
workbook = xlwt.Workbook()
|
||||
|
||||
# 创建一个Sheet对象，可指定名称
|
||||
sheet = workbook.load('Sheet1')
|
||||
|
||||
# 写入数据行
|
||||
for row_index, row_data in enumerate(data):
|
||||
for col_index, cell_data in enumerate(row_data):
|
||||
sheet.write(row_index, col_index, cell_data)
|
||||
|
||||
# 保存Workbook到文件
|
||||
workbook.save(get_cur_time(date)[0] + '.xls')
|
||||
|
||||
|
||||
|
||||
def getNow(date='', offset=0):
|
||||
"""生成指定日期的两种格式字符串
|
||||
Args:
|
||||
date: 支持多种输入类型：
|
||||
- datetime对象
|
||||
- 字符串格式（支持'%Y-%m-%d'和'%Y%m%d'）
|
||||
- 空字符串表示当前日期
|
||||
offset: 日期偏移天数
|
||||
Returns:
|
||||
tuple: (紧凑日期字符串, 标准日期字符串)
|
||||
"""
|
||||
# 日期解析逻辑
|
||||
from datetime import datetime,timedelta
|
||||
if isinstance(date, datetime):
|
||||
now = date
|
||||
else:
|
||||
now = datetime.now()
|
||||
if date:
|
||||
# 尝试多种日期格式解析
|
||||
for fmt in ('%Y-%m-%d', '%Y%m%d', '%Y/%m/%d'):
|
||||
try:
|
||||
now = datetime.strptime(str(date), fmt)
|
||||
break
|
||||
except ValueError:
|
||||
continue
|
||||
else:
|
||||
raise ValueError(f"无法解析的日期格式: {date}")
|
||||
|
||||
# 应用日期偏移
|
||||
now = now - timedelta(days=offset)
|
||||
|
||||
# 统一格式化输出
|
||||
date_str = now.strftime("%Y-%m-%d")
|
||||
compact_date = date_str.replace("-", "")
|
||||
return compact_date, date_str
|
||||
|
||||
|
||||
def get_cur_time(date=''):
|
||||
if date == '':
|
||||
now = datetime.now()
|
||||
# 如果是字符串，尝试解析日期
|
||||
elif isinstance(date, str):
|
||||
now = datetime.strptime(date, '%Y-%m-%d')
|
||||
else:
|
||||
now = date
|
||||
year = now.year
|
||||
month = now.month
|
||||
day = now.day
|
||||
|
||||
if month < 10:
|
||||
month = "0" + str(month)
|
||||
if day < 10:
|
||||
day = "0" + str(day)
|
||||
cur_time = str(year) + str(month) + str(day)
|
||||
cur_time2 = str(year) + "-" + str(month) + "-" + str(day)
|
||||
return cur_time, cur_time2
|
||||
|
||||
|
||||
def get_head_push_auth():
|
||||
login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))
|
||||
text = json.loads(login_res.text)
|
||||
if text["status"]:
|
||||
token = text["data"]["accessToken"]
|
||||
return token
|
||||
else:
|
||||
print("获取认证失败")
|
||||
return None
|
||||
|
||||
|
||||
|
||||
def upload_data_to_system(token_push,date):
|
||||
datavalue = forecast_price()
|
||||
data = {
|
||||
"funcModule": "数据表信息列表",
|
||||
"funcOperation": "新增",
|
||||
"data": [
|
||||
{"dataItemNo": "C01100047|FORECAST_PRICE",
|
||||
"dataDate": getNow(date)[0],
|
||||
"dataStatus": "add",
|
||||
"dataValue": datavalue
|
||||
}
|
||||
|
||||
]
|
||||
}
|
||||
print(data)
|
||||
headers = {"Authorization": token_push}
|
||||
res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))
|
||||
print(res.text)
|
||||
|
||||
|
||||
|
||||
def forecast_price():
|
||||
# df_test = pd.read_csv('定价模型数据收集0212.csv')
|
||||
df_test = pd.read_excel('纯苯数据项.xls',sheet_name='Sheet1')
|
||||
df_test.drop([0],inplace=True)
|
||||
# df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)
|
||||
df_test['Date']=pd.to_datetime(df_test['Date'], format=r'%Y-%m-%d',infer_datetime_format=True)
|
||||
|
||||
|
||||
df_test_1 = df_test
|
||||
df_test_1=df_test_1.fillna(df_test.ffill())
|
||||
df_test_1=df_test_1.fillna(df_test_1.bfill())
|
||||
|
||||
# 选择用于模型训练的列名称
|
||||
col_for_training = df_test_1.columns
|
||||
|
||||
|
||||
import joblib
|
||||
Best_model_DalyLGPrice = joblib.load("日度价格预测_最佳模型.pkl")
|
||||
# 最新的一天为最后一行的数据
|
||||
|
||||
df_test_1_Day = df_test_1.tail(1)
|
||||
# 移除不需要的列
|
||||
df_test_1_Day.index = df_test_1_Day["Date"]
|
||||
df_test_1_Day = df_test_1_Day.drop(["Date"], axis= 1)
|
||||
df_test_1_Day=df_test_1_Day.drop('Price',axis=1)
|
||||
df_test_1_Day=df_test_1_Day.dropna()
|
||||
|
||||
for col in df_test_1_Day.columns:
|
||||
df_test_1_Day[col] = pd.to_numeric(df_test_1_Day[col],errors='coerce')
|
||||
#预测今日价格，显示至小数点后两位
|
||||
Ypredict_Today=Best_model_DalyLGPrice.predict(df_test_1_Day)
|
||||
|
||||
df_test_1_Day['日度预测价格']=Ypredict_Today
|
||||
print(df_test_1_Day['日度预测价格'])
|
||||
a = df_test_1_Day['日度预测价格']
|
||||
a = a[0]
|
||||
a = float(a)
|
||||
a = round(a,2)
|
||||
return a
|
||||
def optimize_Model():
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.impute import SimpleImputer
|
||||
from sklearn.preprocessing import OrdinalEncoder
|
||||
from sklearn.feature_selection import SelectFromModel
|
||||
from sklearn.metrics import mean_squared_error, r2_score
|
||||
import pandas as pd
|
||||
|
||||
pd.set_option('display.max_rows',40)
|
||||
pd.set_option('display.max_columns',40)
|
||||
df_test = pd.read_excel('纯苯数据项.xls')
|
||||
df_test.drop([0],inplace=True)
|
||||
# df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)
|
||||
df_test['Date']=pd.to_datetime(df_test['Date'], format='%Y-%m-%d',infer_datetime_format=True)
|
||||
|
||||
|
||||
#将缺失值补为前一个或者后一个数值
|
||||
df_test_1 = df_test
|
||||
df_test_1=df_test_1.fillna(df_test.ffill())
|
||||
df_test_1=df_test_1.fillna(df_test_1.bfill())
|
||||
df_test_1["Date"] = pd.to_datetime(df_test_1["Date"])
|
||||
df_test_1.index = df_test_1["Date"]
|
||||
df_test_1 = df_test_1.drop(["Date"], axis= 1)
|
||||
df_test_1 = df_test_1.astype('float')
|
||||
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from pandas import Series,DataFrame
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import sklearn.datasets as datasets
|
||||
|
||||
#导入机器学习算法模型
|
||||
from sklearn.linear_model import Lasso
|
||||
from xgboost import XGBRegressor
|
||||
|
||||
import statsmodels.api as sm
|
||||
try:
|
||||
from keras.preprocessing.sequence import TimeseriesGenerator
|
||||
except:
|
||||
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
|
||||
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
|
||||
import xgboost as xgb
|
||||
from xgboost import plot_importance, plot_tree
|
||||
from sklearn.metrics import mean_absolute_error
|
||||
from statsmodels.tools.eval_measures import mse,rmse
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from xgboost import XGBRegressor
|
||||
import warnings
|
||||
import pickle
|
||||
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
#切割训练数据和样本数据
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
#用于模型评分
|
||||
from sklearn.metrics import r2_score
|
||||
|
||||
dataset1=df_test_1.drop('Price',axis=1)#.astype(float)
|
||||
|
||||
y=df_test_1['Price']
|
||||
|
||||
x=dataset1
|
||||
|
||||
train = x
|
||||
target = y
|
||||
|
||||
#切割数据样本集合测试集
|
||||
X_train,x_test,y_train,y_true = train_test_split(train,target,test_size=0.2,random_state=0)
|
||||
|
||||
#模型缩写
|
||||
Lasso = Lasso(random_state=0)
|
||||
XGBR = XGBRegressor(random_state=0)
|
||||
#训练模型
|
||||
Lasso.fit(X_train,y_train)
|
||||
XGBR.fit(X_train,y_train)
|
||||
#模型拟合
|
||||
y_pre_Lasso = Lasso.predict(x_test)
|
||||
y_pre_XGBR = XGBR.predict(x_test)
|
||||
|
||||
#计算Lasso、XGBR、RandomForestR、AdaBoostR、GradientBoostingR、BaggingRegressor各模型的R²
|
||||
Lasso_score = r2_score(y_true,y_pre_Lasso)
|
||||
XGBR_score=r2_score(y_true,y_pre_XGBR)
|
||||
|
||||
#计算Lasso、XGBR的MSE和RMSE
|
||||
Lasso_MSE=mean_squared_error(y_true, y_pre_Lasso)
|
||||
XGBR_MSE=mean_squared_error(y_true, y_pre_XGBR)
|
||||
|
||||
Lasso_RMSE=np.sqrt(Lasso_MSE)
|
||||
XGBR_RMSE=np.sqrt(XGBR_MSE)
|
||||
# 将不同模型的不同误差值整合成一个表格
|
||||
model_results = pd.DataFrame([['Lasso', Lasso_RMSE, Lasso_score],
|
||||
['XgBoost', XGBR_RMSE, XGBR_score]],
|
||||
columns = ['模型(Model)','均方根误差(RMSE)', 'R^2 score'])
|
||||
#将模型名称(Model)列设置为索引
|
||||
model_results1=model_results.set_index('模型(Model)')
|
||||
|
||||
model_results1
|
||||
#定义plot_feature_importance函数，该函数用于计算特征重要性。此部分代码无需调整
|
||||
def plot_feature_importance(importance,names,model_type):
|
||||
feature_importance = np.array(importance)
|
||||
feature_names = np.array(names)
|
||||
|
||||
data={'feature_names':feature_names,'feature_importance':feature_importance}
|
||||
fi_df = pd.DataFrame(data)
|
||||
|
||||
fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)
|
||||
|
||||
plt.figure(figsize=(10,8))
|
||||
sn.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])
|
||||
|
||||
plt.title(model_type + " "+'FEATURE IMPORTANCE')
|
||||
plt.xlabel('FEATURE IMPORTANCE')
|
||||
plt.ylabel('FEATURE NAMES')
|
||||
from pylab import mpl
|
||||
mpl.rcParams['font.sans-serif'] = ['SimHei']
|
||||
## Xgboost 模型参数优化-初步
|
||||
#参考: https://juejin.im/post/6844903661013827598
|
||||
#每次调参时，备选参数数值以同数量级的1、3、10设置即可（比如设置1、3、10，或0.1、0.3、1.0，或0.01,0.03,0.10即可）
|
||||
|
||||
from xgboost import XGBRegressor
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
|
||||
estimator = XGBRegressor(random_state=0,
|
||||
nthread=4,
|
||||
seed=0
|
||||
)
|
||||
parameters = {
|
||||
'max_depth': range (2, 11, 2), # 树的最大深度
|
||||
'n_estimators': range (50, 101, 10), # 迭代次数
|
||||
'learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]
|
||||
}
|
||||
|
||||
grid_search_XGB = GridSearchCV(
|
||||
estimator=estimator,
|
||||
param_grid=parameters,
|
||||
# n_jobs = 10,
|
||||
cv = 3,
|
||||
verbose=True
|
||||
)
|
||||
|
||||
grid_search_XGB.fit(X_train, y_train)
|
||||
#如果电脑在此步骤报错，可能是因为计算量太大，超过硬件可支持程度，可注释掉“n_jobs=10”一行
|
||||
|
||||
best_parameters = grid_search_XGB.best_estimator_.get_params()
|
||||
y_pred = grid_search_XGB.predict(x_test)
|
||||
|
||||
op_XGBR_score = r2_score(y_true,y_pred)
|
||||
op_XGBR_MSE= mean_squared_error(y_true, y_pred)
|
||||
op_XGBR_RMSE= np.sqrt(op_XGBR_MSE)
|
||||
|
||||
model_results2 = pd.DataFrame([['Optimized_Xgboost', op_XGBR_RMSE, op_XGBR_score]],
|
||||
columns = ['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])
|
||||
model_results2=model_results2.set_index('模型(Model)')
|
||||
|
||||
# results = model_results1.append(model_results2, ignore_index = False)
|
||||
results = pd.concat([model_results1,model_results2],ignore_index=True)
|
||||
|
||||
|
||||
|
||||
import pickle
|
||||
|
||||
Pkl_Filename = "日度价格预测_最佳模型.pkl"
|
||||
|
||||
with open(Pkl_Filename, 'wb') as file:
|
||||
pickle.dump(grid_search_XGB, file)
|
||||
|
||||
|
||||
|
||||
|
||||
def read_xls_data():
|
||||
global one_cols, two_cols
|
||||
# 打开 XLS 文件
|
||||
workbook = xlrd.open_workbook(read_file_path_name)
|
||||
|
||||
# 获取所有表格名称
|
||||
# sheet_names = workbook.sheet_names()
|
||||
|
||||
# 选择第一个表格
|
||||
sheet = workbook.sheet_by_index(0)
|
||||
|
||||
# 获取行数和列数
|
||||
num_rows = sheet.nrows
|
||||
# num_cols = sheet.ncols
|
||||
|
||||
# 遍历每一行，获取单元格数据
|
||||
# for i in range(num_rows):
|
||||
# row_data = sheet.row_values(i)
|
||||
# one_cols.append(row_data)
|
||||
# two_cols.append(row_data[1])
|
||||
|
||||
row_data = sheet.row_values(1)
|
||||
print(f'获取到的数据项ID{row_data}')
|
||||
one_cols = row_data[1:]
|
||||
print(f'获取到的数据项ID{one_cols}')
|
||||
|
||||
# 关闭 XLS 文件
|
||||
# workbook.close()
|
||||
|
||||
|
||||
|
||||
|
||||
def start(date=None,token=None,token_push=None):
|
||||
read_xls_data()
|
||||
if date == None:
|
||||
date = getNow()[0]
|
||||
if token == None:
|
||||
token = get_head_auth()
|
||||
token_push = get_head_push_auth()
|
||||
|
||||
datas = get_data_value(token, one_cols,date)
|
||||
if not datas:
|
||||
print("今天没有新数据")
|
||||
return
|
||||
|
||||
# data_list = [two_cols, one_cols]
|
||||
append_rows = [getNow(date)[1]]
|
||||
# append_rows = [getNow()[1]]
|
||||
dataItemNo_dataValue = {}
|
||||
for data_value in datas:
|
||||
if "dataValue" not in data_value:
|
||||
print(data_value)
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = ""
|
||||
else:
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
|
||||
|
||||
for value in one_cols:
|
||||
if value in dataItemNo_dataValue:
|
||||
append_rows.append(dataItemNo_dataValue[value])
|
||||
else:
|
||||
append_rows.append("")
|
||||
save_xls(append_rows)
|
||||
|
||||
# 获取当月的数据写入到指定文件，如果是补充数据，不需要执行
|
||||
queryDataListItemNos()
|
||||
# 模型训练
|
||||
optimize_Model()
|
||||
# 上传预测数据
|
||||
upload_data_to_system(token_push,date)
|
||||
# data_list.append(three_cols)
|
||||
# write_xls(data_list)
|
||||
|
||||
|
||||
def start_1(date=None):
|
||||
read_xls_data()
|
||||
if date == None:
|
||||
date = getNow(offset=1)[0]
|
||||
token = get_head_auth()
|
||||
if not token:
|
||||
return
|
||||
|
||||
|
||||
datas = get_data_value(token, one_cols,date=date)
|
||||
# if not datas:
|
||||
# return
|
||||
|
||||
# data_list = [two_cols, one_cols]
|
||||
append_rows = [getNow(offset=1)[1]]
|
||||
dataItemNo_dataValue = {}
|
||||
for data_value in datas:
|
||||
if "dataValue" not in data_value:
|
||||
print(data_value)
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = ""
|
||||
else:
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
|
||||
|
||||
for value in one_cols:
|
||||
if value in dataItemNo_dataValue:
|
||||
append_rows.append(dataItemNo_dataValue[value])
|
||||
else:
|
||||
append_rows.append("")
|
||||
save_xls_1(append_rows)
|
||||
|
||||
|
||||
# data_list.append(three_cols)
|
||||
# write_xls(data_list)
|
||||
|
||||
def save_xls_1(append_rows):
|
||||
|
||||
# 打开xls文件
|
||||
workbook = xlrd.open_workbook('纯苯数据项.xls')
|
||||
|
||||
# 获取所有sheet的个数
|
||||
sheet_count = len(workbook.sheet_names())
|
||||
|
||||
# 获取所有sheet的名称
|
||||
sheet_names = workbook.sheet_names()
|
||||
|
||||
new_workbook = xlwt.Workbook()
|
||||
for i in range(sheet_count):
|
||||
# 获取当前sheet
|
||||
sheet = workbook.sheet_by_index(i)
|
||||
|
||||
# 获取sheet的行数和列数
|
||||
row_count = sheet.nrows - 1
|
||||
col_count = sheet.ncols
|
||||
# 获取原有数据
|
||||
data = []
|
||||
for row in range(row_count):
|
||||
row_data = []
|
||||
for col in range(col_count):
|
||||
row_data.append(sheet.cell_value(row, col))
|
||||
data.append(row_data)
|
||||
# 创建xlwt的Workbook对象
|
||||
# 创建sheet
|
||||
new_sheet = new_workbook.add_sheet(sheet_names[i])
|
||||
|
||||
# 将原有的数据写入新的sheet
|
||||
for row in range(row_count):
|
||||
for col in range(col_count):
|
||||
new_sheet.write(row, col, data[row][col])
|
||||
|
||||
if i == 0:
|
||||
# 在新的sheet中添加数据
|
||||
for col in range(col_count):
|
||||
new_sheet.write(row_count, col, append_rows[col])
|
||||
|
||||
# 保存新的xls文件
|
||||
new_workbook.save("纯苯数据项.xls")
|
||||
|
||||
|
||||
|
||||
|
||||
def check_data(dataItemNo):
|
||||
token = get_head_auth()
|
||||
if not token:
|
||||
return
|
||||
|
||||
datas = get_data_value(token, dataItemNo)
|
||||
if not datas:
|
||||
return
|
||||
|
||||
|
||||
def save_xls(append_rows):
|
||||
|
||||
# 打开xls文件
|
||||
workbook = xlrd.open_workbook('纯苯数据项.xls')
|
||||
|
||||
# 获取所有sheet的个数
|
||||
sheet_count = len(workbook.sheet_names())
|
||||
|
||||
# 获取所有sheet的名称
|
||||
sheet_names = workbook.sheet_names()
|
||||
|
||||
new_workbook = xlwt.Workbook()
|
||||
for i in range(sheet_count):
|
||||
# 获取当前sheet
|
||||
sheet = workbook.sheet_by_index(i)
|
||||
|
||||
# 获取sheet的行数和列数
|
||||
row_count = sheet.nrows
|
||||
col_count = sheet.ncols
|
||||
# 获取原有数据
|
||||
data = []
|
||||
for row in range(row_count):
|
||||
row_data = []
|
||||
for col in range(col_count):
|
||||
row_data.append(sheet.cell_value(row, col))
|
||||
data.append(row_data)
|
||||
# 创建xlwt的Workbook对象
|
||||
# 创建sheet
|
||||
new_sheet = new_workbook.add_sheet(sheet_names[i])
|
||||
|
||||
# 将原有的数据写入新的sheet
|
||||
for row in range(row_count):
|
||||
for col in range(col_count):
|
||||
new_sheet.write(row, col, data[row][col])
|
||||
|
||||
if i == 0:
|
||||
# 在新的sheet中添加数据
|
||||
for col in range(col_count):
|
||||
new_sheet.write(row_count, col, append_rows[col])
|
||||
|
||||
# 保存新的xls文件
|
||||
new_workbook.save("纯苯数据项.xls")
|
||||
|
||||
|
||||
|
||||
|
||||
def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd):
|
||||
|
||||
search_data = {
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询",
|
||||
"data": {
|
||||
"dateStart": dateStart,
|
||||
"dateEnd": dateEnd,
|
||||
"dataItemNoList": dataItemNoList # 数据项编码,代表 brent最低价和最高价
|
||||
}
|
||||
}
|
||||
|
||||
headers = {"Authorization": token}
|
||||
search_res = requests.post(url=url, headers=headers, json=search_data, timeout=(3, 5))
|
||||
search_value = json.loads(search_res.text)["data"]
|
||||
if search_value:
|
||||
return search_value
|
||||
else:
|
||||
return None
|
||||
|
||||
|
||||
|
||||
def save_queryDataListItemNos_xls(data_df,dataItemNoList):
|
||||
from datetime import datetime,timedelta
|
||||
current_year_month = datetime.now().strftime('%Y-%m')
|
||||
grouped = data_df.groupby("dataDate")
|
||||
|
||||
# 打开xls文件
|
||||
workbook = xlrd.open_workbook('纯苯数据项.xls')
|
||||
|
||||
# 获取所有sheet的个数
|
||||
sheet_count = len(workbook.sheet_names())
|
||||
|
||||
# 获取所有sheet的名称
|
||||
sheet_names = workbook.sheet_names()
|
||||
|
||||
new_workbook = xlwt.Workbook()
|
||||
for i in range(sheet_count):
|
||||
# 获取当前sheet
|
||||
sheet = workbook.sheet_by_index(i)
|
||||
|
||||
# 获取sheet的行数和列数
|
||||
row_count = sheet.nrows
|
||||
col_count = sheet.ncols
|
||||
# 获取原有数据
|
||||
data = []
|
||||
for row in range(row_count):
|
||||
row_data = []
|
||||
for col in range(col_count):
|
||||
row_data.append(sheet.cell_value(row, col))
|
||||
data.append(row_data)
|
||||
|
||||
# 创建xlwt的Workbook对象
|
||||
# 创建sheet
|
||||
new_sheet = new_workbook.add_sheet(sheet_names[i])
|
||||
|
||||
|
||||
current_year_month_row = 0
|
||||
# 将原有的数据写入新的sheet
|
||||
for row in range(row_count):
|
||||
for col in range(col_count):
|
||||
col0 = data[row][0]
|
||||
# print("col0",col0[:7])
|
||||
if col0[:7] == current_year_month:
|
||||
current_year_month_row += 1
|
||||
break
|
||||
new_sheet.write(row, col, data[row][col])
|
||||
|
||||
|
||||
# print("current_year_month_row",current_year_month_row)
|
||||
if i == 0:
|
||||
rowFlag = 0
|
||||
# 查看每组数据
|
||||
for date, group in grouped:
|
||||
new_sheet.write(row_count + rowFlag - current_year_month_row, 0, date)
|
||||
for j in range(len(dataItemNoList)):
|
||||
dataItemNo = dataItemNoList[j]
|
||||
|
||||
if group[group["dataItemNo"] == dataItemNo]["dataValue"].values and (not str(group[group["dataItemNo"] == dataItemNo]["dataValue"].values[0]) == 'nan'):
|
||||
|
||||
new_sheet.write(row_count + rowFlag - current_year_month_row, j + 1, group[group["dataItemNo"] == dataItemNo]["dataValue"].values[0])
|
||||
|
||||
rowFlag += 1
|
||||
|
||||
|
||||
# 保存新的xls文件
|
||||
new_workbook.save("纯苯数据项.xls")
|
||||
|
||||
|
||||
|
||||
def queryDataListItemNos(date=None,token=None):
|
||||
from datetime import datetime, timedelta
|
||||
df = pd.read_excel('纯苯数据项.xls')
|
||||
dataItemNoList = df.iloc[0].tolist()[1:]
|
||||
if token is None:
|
||||
token = get_head_auth()
|
||||
if not token:
|
||||
print('token获取失败')
|
||||
return
|
||||
# 获取当前日期
|
||||
if date is None:
|
||||
current_date = datetime.now()
|
||||
else:
|
||||
current_date = date
|
||||
# 获取当月1日
|
||||
first_day_of_month = current_date.replace(day=1)
|
||||
# 格式化为 YYYYMMDD 格式
|
||||
dateEnd = current_date.strftime('%Y%m%d')
|
||||
dateStart = first_day_of_month.strftime('%Y%m%d')
|
||||
search_value = get_queryDataListItemNos_value(token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd)
|
||||
data_df = pd.DataFrame(search_value)
|
||||
data_df["dataDate"] = pd.to_datetime(data_df["dataDate"])
|
||||
data_df["dataDate"] = data_df["dataDate"].dt.strftime('%Y-%m-%d')
|
||||
save_queryDataListItemNos_xls(data_df,dataItemNoList)
|
||||
print('当月数据更新完成')
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print('运行中ing')
|
||||
start()
|
||||
|
||||
Binary file not shown.
632
aisenzhecode/聚合级丙烯/丙烯每日价格预测.py
Normal file
632
aisenzhecode/聚合级丙烯/丙烯每日价格预测.py
Normal file
@ -0,0 +1,632 @@
|
||||
import requests
|
||||
import json
|
||||
import xlrd
|
||||
import xlwt
|
||||
from datetime import datetime
|
||||
import time
|
||||
# 变量定义
|
||||
login_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
search_url = "http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos"
|
||||
|
||||
login_push_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
upload_url = "http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
login_push_data = {
|
||||
"data": {
|
||||
"account": "api_dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
read_file_path_name = "丙烯基础数据收集表.xls"
|
||||
one_cols = []
|
||||
two_cols = []
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sn
|
||||
import random
|
||||
import time
|
||||
|
||||
|
||||
|
||||
|
||||
from plotly import __version__
|
||||
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
|
||||
|
||||
from sklearn import preprocessing
|
||||
|
||||
from pandas import Series,DataFrame
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import sklearn.datasets as datasets
|
||||
|
||||
#导入机器学习算法模型
|
||||
from sklearn.linear_model import Lasso
|
||||
from xgboost import XGBRegressor
|
||||
|
||||
import datetime
|
||||
import statsmodels.api as sm
|
||||
from keras.preprocessing.sequence import TimeseriesGenerator
|
||||
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
|
||||
import xgboost as xgb
|
||||
from xgboost import plot_importance, plot_tree
|
||||
from sklearn.metrics import mean_absolute_error
|
||||
from statsmodels.tools.eval_measures import mse,rmse
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from xgboost import XGBRegressor
|
||||
import warnings
|
||||
import pickle
|
||||
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
#切割训练数据和样本数据
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
#用于模型评分
|
||||
from sklearn.metrics import r2_score
|
||||
|
||||
le = preprocessing.LabelEncoder()
|
||||
|
||||
# print(__version__) # requires version >= 1.9.0
|
||||
|
||||
|
||||
import cufflinks as cf
|
||||
cf.go_offline()
|
||||
|
||||
random.seed(100)
|
||||
|
||||
|
||||
|
||||
# 数据获取
|
||||
|
||||
def get_head_auth():
|
||||
login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))
|
||||
text = json.loads(login_res.text)
|
||||
if text["status"]:
|
||||
token = text["data"]["accessToken"]
|
||||
return token
|
||||
else:
|
||||
print("获取认证失败")
|
||||
return None
|
||||
|
||||
|
||||
def get_data_value(token, dataItemNoList):
|
||||
search_data = {
|
||||
"data": {
|
||||
"date": get_cur_time()[0],
|
||||
"dataItemNoList": dataItemNoList
|
||||
},
|
||||
"funcModule": "数据项",
|
||||
"funcOperation": "查询"
|
||||
}
|
||||
headers = {"Authorization": token}
|
||||
search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))
|
||||
search_value = json.loads(search_res.text)["data"]
|
||||
if search_value:
|
||||
return search_value
|
||||
else:
|
||||
print("今天没有新数据")
|
||||
return search_value
|
||||
|
||||
|
||||
# xls文件处理
|
||||
|
||||
def write_xls(data):
|
||||
# 创建一个Workbook对象
|
||||
workbook = xlwt.Workbook()
|
||||
|
||||
# 创建一个Sheet对象，可指定名称
|
||||
sheet = workbook.load('Sheet1')
|
||||
|
||||
# 写入数据行
|
||||
for row_index, row_data in enumerate(data):
|
||||
for col_index, cell_data in enumerate(row_data):
|
||||
sheet.write(row_index, col_index, cell_data)
|
||||
|
||||
# 保存Workbook到文件
|
||||
workbook.save(get_cur_time()[0] + '.xls')
|
||||
|
||||
|
||||
def get_cur_time():
|
||||
now = datetime.datetime.now()
|
||||
year = now.year
|
||||
month = now.month
|
||||
day = now.day
|
||||
|
||||
if month < 10:
|
||||
month = "0" + str(month)
|
||||
if day < 10:
|
||||
day = "0" + str(day)
|
||||
cur_time = str(year) + str(month) + str(day)
|
||||
cur_time2 = str(year) + "-" + str(month) + "-" + str(day)
|
||||
# cur_time = '20231011'
|
||||
# cur_time2 = '2023-10-11'
|
||||
return cur_time, cur_time2
|
||||
|
||||
|
||||
def get_head_push_auth():
|
||||
login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))
|
||||
text = json.loads(login_res.text)
|
||||
if text["status"]:
|
||||
token = text["data"]["accessToken"]
|
||||
return token
|
||||
else:
|
||||
print("获取认证失败")
|
||||
return None
|
||||
|
||||
|
||||
|
||||
def upload_data_to_system(token_push):
|
||||
data = {
|
||||
"funcModule": "数据表信息列表",
|
||||
"funcOperation": "新增",
|
||||
"data": [
|
||||
{"dataItemNo": "C01100007|Forecast_Price|ACN",
|
||||
"dataDate": get_cur_time()[0],
|
||||
"dataStatus": "add",
|
||||
# "dataValue": 7100
|
||||
"dataValue": forecast_price()
|
||||
}
|
||||
|
||||
]
|
||||
}
|
||||
headers = {"Authorization": token_push}
|
||||
res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))
|
||||
print(res.text)
|
||||
|
||||
|
||||
# def upload_data_to_system(token):
|
||||
# data = {
|
||||
# "funcModule": "数据表信息列表",
|
||||
# "funcOperation": "新增",
|
||||
# "data": [
|
||||
# {"dataItemNo": "C01100036|Forecast_ Price|ACN",
|
||||
# "dataDate": '20230706',
|
||||
# "dataStatus": "add",
|
||||
# "dataValue": 3780.0
|
||||
# }
|
||||
|
||||
# ]
|
||||
# }
|
||||
# headers = {"Authorization": token}
|
||||
# res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))
|
||||
# print(res.text)
|
||||
|
||||
|
||||
|
||||
def forecast_price():
|
||||
# df_test = pd.read_csv('定价模型数据收集0212.csv')
|
||||
df_test = pd.read_excel('丙烯基础数据收集表.xls')
|
||||
df_test.drop([0],inplace=True)
|
||||
df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)
|
||||
|
||||
#将缺失值补为前一个或者后一个数值
|
||||
df_test_1 = df_test
|
||||
df_test_1=df_test_1.fillna(df_test.ffill())
|
||||
df_test_1=df_test_1.fillna(df_test_1.bfill())
|
||||
|
||||
# 选择用于模型训练的列名称
|
||||
col_for_training = df_test_1.columns
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
import joblib
|
||||
Best_model_DalyLGPrice = joblib.load("日度价格预测_丙烯最佳模型.pkl")
|
||||
# 最新的一天为最后一行的数据
|
||||
|
||||
df_test_1_Day = df_test_1.tail(1)
|
||||
# 移除不需要的列
|
||||
df_test_1_Day.index = df_test_1_Day["Date"]
|
||||
df_test_1_Day = df_test_1_Day.drop(["Date"], axis= 1)
|
||||
df_test_1_Day=df_test_1_Day.drop('Price',axis=1)
|
||||
df_test_1_Day=df_test_1_Day.dropna()
|
||||
|
||||
# df_test_1_Day
|
||||
#预测今日价格，显示至小数点后两位
|
||||
Ypredict_Today=Best_model_DalyLGPrice.predict(df_test_1_Day)
|
||||
|
||||
df_test_1_Day['日度预测价格']=Ypredict_Today
|
||||
print(df_test_1_Day['日度预测价格'])
|
||||
a = df_test_1_Day['日度预测价格']
|
||||
a = a[0]
|
||||
a = float(a)
|
||||
a = round(a,2)
|
||||
return a
|
||||
def optimize_Model():
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.impute import SimpleImputer
|
||||
from sklearn.preprocessing import OrdinalEncoder
|
||||
from sklearn.feature_selection import SelectFromModel
|
||||
from sklearn.metrics import mean_squared_error, r2_score
|
||||
import pandas as pd
|
||||
|
||||
pd.set_option('display.max_rows',40)
|
||||
pd.set_option('display.max_columns',40)
|
||||
df_test = pd.read_excel('丙烯基础数据收集表.xls')
|
||||
df_test.drop([0],inplace=True)
|
||||
df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)
|
||||
#查看每个特征缺失值数量
|
||||
MisVal_Check=df_test.isnull().sum().sort_values(ascending=False)
|
||||
#去掉缺失值百分比>0.4的特征，去掉这些特征后的新表格命名为df_test_1
|
||||
df_MisVal_Check = pd.DataFrame(MisVal_Check,)#
|
||||
df_MisVal_Check_1=df_MisVal_Check.reset_index()
|
||||
df_MisVal_Check_1.columns=['Variable_Name','Missing_Number']
|
||||
df_MisVal_Check_1['Missing_Number']=df_MisVal_Check_1['Missing_Number']/len(df_test)
|
||||
df_test_1=df_test.drop(df_MisVal_Check_1[df_MisVal_Check_1['Missing_Number']>0.4].Variable_Name,axis = 1)
|
||||
#将缺失值补为前一个或者后一个数值
|
||||
df_test_1 = df_test
|
||||
df_test_1=df_test_1.fillna(df_test.ffill())
|
||||
df_test_1=df_test_1.fillna(df_test_1.bfill())
|
||||
df_test_1["Date"] = pd.to_datetime(df_test_1["Date"])
|
||||
df_test_1.index = df_test_1["Date"]
|
||||
df_test_1 = df_test_1.drop(["Date"], axis= 1)
|
||||
df_test_1 = df_test_1.astype('float')
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from pandas import Series,DataFrame
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import sklearn.datasets as datasets
|
||||
|
||||
#导入机器学习算法模型
|
||||
from sklearn.linear_model import Lasso
|
||||
from xgboost import XGBRegressor
|
||||
|
||||
from datetime import datetime
|
||||
import statsmodels.api as sm
|
||||
from keras.preprocessing.sequence import TimeseriesGenerator
|
||||
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
|
||||
import xgboost as xgb
|
||||
from xgboost import plot_importance, plot_tree
|
||||
from sklearn.metrics import mean_absolute_error
|
||||
from statsmodels.tools.eval_measures import mse,rmse
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from xgboost import XGBRegressor
|
||||
import warnings
|
||||
import pickle
|
||||
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
#切割训练数据和样本数据
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
#用于模型评分
|
||||
from sklearn.metrics import r2_score
|
||||
|
||||
dataset1=df_test_1.drop('Price',axis=1)#.astype(float)
|
||||
|
||||
y=df_test_1['Price']
|
||||
|
||||
x=dataset1
|
||||
|
||||
train = x
|
||||
target = y
|
||||
|
||||
#切割数据样本集合测试集
|
||||
X_train,x_test,y_train,y_true = train_test_split(train,target,test_size=0.2,random_state=0)
|
||||
|
||||
#模型缩写
|
||||
Lasso = Lasso(random_state=0)
|
||||
XGBR = XGBRegressor(random_state=0)
|
||||
#训练模型
|
||||
Lasso.fit(X_train,y_train)
|
||||
XGBR.fit(X_train,y_train)
|
||||
#模型拟合
|
||||
y_pre_Lasso = Lasso.predict(x_test)
|
||||
y_pre_XGBR = XGBR.predict(x_test)
|
||||
|
||||
#计算Lasso、XGBR、RandomForestR、AdaBoostR、GradientBoostingR、BaggingRegressor各模型的R²
|
||||
Lasso_score = r2_score(y_true,y_pre_Lasso)
|
||||
XGBR_score=r2_score(y_true,y_pre_XGBR)
|
||||
|
||||
#计算Lasso、XGBR的MSE和RMSE
|
||||
Lasso_MSE=mean_squared_error(y_true, y_pre_Lasso)
|
||||
XGBR_MSE=mean_squared_error(y_true, y_pre_XGBR)
|
||||
|
||||
Lasso_RMSE=np.sqrt(Lasso_MSE)
|
||||
XGBR_RMSE=np.sqrt(XGBR_MSE)
|
||||
# 将不同模型的不同误差值整合成一个表格
|
||||
model_results = pd.DataFrame([['Lasso', Lasso_RMSE, Lasso_score],
|
||||
['XgBoost', XGBR_RMSE, XGBR_score]],
|
||||
columns = ['模型(Model)','均方根误差(RMSE)', 'R^2 score'])
|
||||
#将模型名称(Model)列设置为索引
|
||||
model_results1=model_results.set_index('模型(Model)')
|
||||
|
||||
model_results1
|
||||
#定义plot_feature_importance函数，该函数用于计算特征重要性。此部分代码无需调整
|
||||
def plot_feature_importance(importance,names,model_type):
|
||||
feature_importance = np.array(importance)
|
||||
feature_names = np.array(names)
|
||||
|
||||
data={'feature_names':feature_names,'feature_importance':feature_importance}
|
||||
fi_df = pd.DataFrame(data)
|
||||
|
||||
fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)
|
||||
|
||||
plt.figure(figsize=(10,8))
|
||||
sn.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])
|
||||
|
||||
plt.title(model_type + " "+'FEATURE IMPORTANCE')
|
||||
plt.xlabel('FEATURE IMPORTANCE')
|
||||
plt.ylabel('FEATURE NAMES')
|
||||
from pylab import mpl
|
||||
mpl.rcParams['font.sans-serif'] = ['SimHei']
|
||||
## Xgboost 模型参数优化-初步
|
||||
#参考: https://juejin.im/post/6844903661013827598
|
||||
#每次调参时，备选参数数值以同数量级的1、3、10设置即可（比如设置1、3、10，或0.1、0.3、1.0，或0.01,0.03,0.10即可）
|
||||
|
||||
from xgboost import XGBRegressor
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
|
||||
estimator = XGBRegressor(random_state=0,
|
||||
nthread=4,
|
||||
seed=0
|
||||
)
|
||||
parameters = {
|
||||
'max_depth': range (2, 11, 2), # 树的最大深度
|
||||
'n_estimators': range (50, 101, 10), # 迭代次数
|
||||
'learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]
|
||||
}
|
||||
|
||||
grid_search_XGB = GridSearchCV(
|
||||
estimator=estimator,
|
||||
param_grid=parameters,
|
||||
# n_jobs = 10,
|
||||
cv = 3,
|
||||
verbose=True
|
||||
)
|
||||
|
||||
grid_search_XGB.fit(X_train, y_train)
|
||||
#如果电脑在此步骤报错，可能是因为计算量太大，超过硬件可支持程度，可注释掉“n_jobs=10”一行
|
||||
|
||||
best_parameters = grid_search_XGB.best_estimator_.get_params()
|
||||
y_pred = grid_search_XGB.predict(x_test)
|
||||
|
||||
op_XGBR_score = r2_score(y_true,y_pred)
|
||||
op_XGBR_MSE= mean_squared_error(y_true, y_pred)
|
||||
op_XGBR_RMSE= np.sqrt(op_XGBR_MSE)
|
||||
|
||||
model_results2 = pd.DataFrame([['Optimized_Xgboost', op_XGBR_RMSE, op_XGBR_score]],
|
||||
columns = ['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])
|
||||
model_results2=model_results2.set_index('模型(Model)')
|
||||
|
||||
results = model_results1.append(model_results2, ignore_index = False)
|
||||
import pickle
|
||||
|
||||
Pkl_Filename = "日度价格预测_丙烯最佳模型.pkl"
|
||||
|
||||
with open(Pkl_Filename, 'wb') as file:
|
||||
pickle.dump(grid_search_XGB, file)
|
||||
|
||||
|
||||
|
||||
|
||||
def read_xls_data():
|
||||
global one_cols, two_cols
|
||||
# 打开 XLS 文件
|
||||
workbook = xlrd.open_workbook(read_file_path_name)
|
||||
|
||||
# 获取所有表格名称
|
||||
# sheet_names = workbook.sheet_names()
|
||||
|
||||
# 选择第一个表格
|
||||
sheet = workbook.sheet_by_index(0)
|
||||
|
||||
# 获取行数和列数
|
||||
num_rows = sheet.nrows
|
||||
# num_cols = sheet.ncols
|
||||
|
||||
# 遍历每一行，获取单元格数据
|
||||
# for i in range(num_rows):
|
||||
# row_data = sheet.row_values(i)
|
||||
# one_cols.append(row_data)
|
||||
# two_cols.append(row_data[1])
|
||||
|
||||
row_data = sheet.row_values(1)
|
||||
one_cols = row_data
|
||||
|
||||
# 关闭 XLS 文件
|
||||
# workbook.close()
|
||||
|
||||
|
||||
|
||||
|
||||
def start():
|
||||
read_xls_data()
|
||||
|
||||
token = get_head_auth()
|
||||
if not token:
|
||||
return
|
||||
token_push = get_head_push_auth()
|
||||
if not token_push:
|
||||
return
|
||||
|
||||
datas = get_data_value(token, one_cols[1:])
|
||||
# if not datas:
|
||||
# return
|
||||
|
||||
# data_list = [two_cols, one_cols]
|
||||
append_rows = [get_cur_time()[1]]
|
||||
dataItemNo_dataValue = {}
|
||||
for data_value in datas:
|
||||
if "dataValue" not in data_value:
|
||||
print(data_value)
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = ""
|
||||
else:
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
|
||||
|
||||
for value in one_cols[1:]:
|
||||
if value in dataItemNo_dataValue:
|
||||
append_rows.append(dataItemNo_dataValue[value])
|
||||
else:
|
||||
append_rows.append("")
|
||||
save_xls(append_rows)
|
||||
optimize_Model()
|
||||
upload_data_to_system(token_push)
|
||||
# data_list.append(three_cols)
|
||||
# write_xls(data_list)
|
||||
|
||||
|
||||
def start_1():
|
||||
read_xls_data()
|
||||
|
||||
token = get_head_auth()
|
||||
if not token:
|
||||
return
|
||||
|
||||
|
||||
datas = get_data_value(token, one_cols[1:])
|
||||
# if not datas:
|
||||
# return
|
||||
|
||||
# data_list = [two_cols, one_cols]
|
||||
append_rows = [get_cur_time()[1]]
|
||||
dataItemNo_dataValue = {}
|
||||
for data_value in datas:
|
||||
if "dataValue" not in data_value:
|
||||
print(data_value)
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = ""
|
||||
else:
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
|
||||
|
||||
for value in one_cols[1:]:
|
||||
if value in dataItemNo_dataValue:
|
||||
append_rows.append(dataItemNo_dataValue[value])
|
||||
else:
|
||||
append_rows.append("")
|
||||
save_xls_1(append_rows)
|
||||
|
||||
|
||||
# data_list.append(three_cols)
|
||||
# write_xls(data_list)
|
||||
|
||||
def save_xls_1(append_rows):
|
||||
|
||||
# 打开xls文件
|
||||
workbook = xlrd.open_workbook('丙烯基础数据收集表.xls')
|
||||
|
||||
# 获取所有sheet的个数
|
||||
sheet_count = len(workbook.sheet_names())
|
||||
|
||||
# 获取所有sheet的名称
|
||||
sheet_names = workbook.sheet_names()
|
||||
|
||||
new_workbook = xlwt.Workbook()
|
||||
for i in range(sheet_count):
|
||||
# 获取当前sheet
|
||||
sheet = workbook.sheet_by_index(i)
|
||||
|
||||
# 获取sheet的行数和列数
|
||||
row_count = sheet.nrows - 1
|
||||
col_count = sheet.ncols
|
||||
# 获取原有数据
|
||||
data = []
|
||||
for row in range(row_count):
|
||||
row_data = []
|
||||
for col in range(col_count):
|
||||
row_data.append(sheet.cell_value(row, col))
|
||||
data.append(row_data)
|
||||
# 创建xlwt的Workbook对象
|
||||
# 创建sheet
|
||||
new_sheet = new_workbook.add_sheet(sheet_names[i])
|
||||
|
||||
# 将原有的数据写入新的sheet
|
||||
for row in range(row_count):
|
||||
for col in range(col_count):
|
||||
new_sheet.write(row, col, data[row][col])
|
||||
|
||||
if i == 0:
|
||||
# 在新的sheet中添加数据
|
||||
for col in range(col_count):
|
||||
new_sheet.write(row_count, col, append_rows[col])
|
||||
|
||||
# 保存新的xls文件
|
||||
new_workbook.save("丙烯基础数据收集表.xls")
|
||||
|
||||
|
||||
|
||||
|
||||
def check_data(dataItemNo):
|
||||
token = get_head_auth()
|
||||
if not token:
|
||||
return
|
||||
|
||||
datas = get_data_value(token, dataItemNo)
|
||||
if not datas:
|
||||
return
|
||||
|
||||
|
||||
def save_xls(append_rows):
|
||||
|
||||
# 打开xls文件
|
||||
workbook = xlrd.open_workbook('丙烯基础数据收集表.xls')
|
||||
|
||||
# 获取所有sheet的个数
|
||||
sheet_count = len(workbook.sheet_names())
|
||||
|
||||
# 获取所有sheet的名称
|
||||
sheet_names = workbook.sheet_names()
|
||||
|
||||
new_workbook = xlwt.Workbook()
|
||||
for i in range(sheet_count):
|
||||
# 获取当前sheet
|
||||
sheet = workbook.sheet_by_index(i)
|
||||
|
||||
# 获取sheet的行数和列数
|
||||
row_count = sheet.nrows
|
||||
col_count = sheet.ncols
|
||||
# 获取原有数据
|
||||
data = []
|
||||
for row in range(row_count):
|
||||
row_data = []
|
||||
for col in range(col_count):
|
||||
row_data.append(sheet.cell_value(row, col))
|
||||
data.append(row_data)
|
||||
# 创建xlwt的Workbook对象
|
||||
# 创建sheet
|
||||
new_sheet = new_workbook.add_sheet(sheet_names[i])
|
||||
|
||||
# 将原有的数据写入新的sheet
|
||||
for row in range(row_count):
|
||||
for col in range(col_count):
|
||||
new_sheet.write(row, col, data[row][col])
|
||||
|
||||
if i == 0:
|
||||
# 在新的sheet中添加数据
|
||||
for col in range(col_count):
|
||||
new_sheet.write(row_count, col, append_rows[col])
|
||||
|
||||
# 保存新的xls文件
|
||||
new_workbook.save("丙烯基础数据收集表.xls")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
start()
|
||||
|
||||
BIN
aisenzhecode/聚合级丙烯/日度价格预测_丙烯最佳模型.pkl
Normal file
BIN
aisenzhecode/聚合级丙烯/日度价格预测_丙烯最佳模型.pkl
Normal file
Binary file not shown.
@ -101,9 +101,9 @@ query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/d
|
||||
# 上传数据项值
|
||||
push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
# 上传停更数据到市场信息平台
|
||||
push_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
|
||||
push_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
|
||||
# 获取预警数据中取消订阅指标ID
|
||||
get_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/dataList"
|
||||
get_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/dataList"
|
||||
|
||||
|
||||
login_data = {
|
||||
|
||||
@ -391,7 +391,7 @@ table_name = 'v_tbl_crude_oil_warning'
|
||||
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 = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
|
||||
is_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
|
||||
|
||||
@ -328,7 +328,7 @@ table_name = 'v_tbl_crude_oil_warning'
|
||||
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 = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
|
||||
is_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
|
||||
|
||||
@ -1026,7 +1026,10 @@ def model_losss(sqlitedb, end_time):
|
||||
model_results3.to_csv(os.path.join(
|
||||
config.dataset, "model_evaluation.csv"), index=False)
|
||||
modelnames = model_results3['模型(Model)'].tolist()
|
||||
most_model_name = modelnames[0]
|
||||
if most_model_name == '':
|
||||
most_model_name = modelnames[0]
|
||||
else:
|
||||
pass
|
||||
allmodelnames = modelnames.copy()
|
||||
# 保存5个最佳模型的名称
|
||||
if len(modelnames) > 5:
|
||||
@ -1329,7 +1332,8 @@ def model_losss(sqlitedb, end_time):
|
||||
|
||||
# for model in most_model:
|
||||
# plt.plot(df['ds'], df[model], label=model,marker='o')
|
||||
plt.plot(df['ds'], df[most_model_name], label=model, marker='o')
|
||||
plt.plot(df['ds'], df[most_model_name],
|
||||
label=most_model_name, marker='o')
|
||||
# 当前日期画竖虚线
|
||||
plt.axvline(x=df['ds'].iloc[-config.horizon],
|
||||
color='r', linestyle='--')
|
||||
|
||||
BIN
日度价格预测_最佳模型.pkl
BIN
日度价格预测_最佳模型.pkl
Binary file not shown.
Loading…
Reference in New Issue
Block a user