石油苯，液化气线上bug调试
This commit is contained in:
workpc
parent
91abe4cfb7
commit
035b74a617
Binary file not shown.
@ -2,7 +2,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -10,12 +10,12 @@
|
||||
"text/html": [
|
||||
" <script type=\"text/javascript\">\n",
|
||||
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
|
||||
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
|
||||
" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.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",
|
||||
" 'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
|
||||
" }\n",
|
||||
" });\n",
|
||||
" require(['plotly'], function(Plotly) {\n",
|
||||
@ -196,36 +196,19 @@
|
||||
" \"funcOperation\": \"新增\",\n",
|
||||
" \"data\": [\n",
|
||||
" {\"dataItemNo\": \"250855713|Forecast_Price|ACN\",\n",
|
||||
" \"dataDate\": date,\n",
|
||||
" \"dataDate\": getNow(date=date)[0],\n",
|
||||
" \"dataStatus\": \"add\",\n",
|
||||
" \"dataValue\": forecast_price()\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" ]\n",
|
||||
" }\n",
|
||||
" # headers = {\"Authorization\": token_push}\n",
|
||||
" # res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
|
||||
" # print(res.text)\n",
|
||||
" headers = {\"Authorization\": token_push}\n",
|
||||
" res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
|
||||
" print(res.text)\n",
|
||||
" print('预测值：',data['data'][0]['dataValue'])\n",
|
||||
"\n",
|
||||
" \n",
|
||||
"# def upload_data_to_system(token):\n",
|
||||
"# data = {\n",
|
||||
"# \"funcModule\": \"数据表信息列表\",\n",
|
||||
"# \"funcOperation\": \"新增\",\n",
|
||||
"# \"data\": [\n",
|
||||
"# {\"dataItemNo\": \"C01100036|Forecast_ Price|ACN\",\n",
|
||||
"# \"dataDate\": '20230706',\n",
|
||||
"# \"dataStatus\": \"add\",\n",
|
||||
"# \"dataValue\": 3780.0\n",
|
||||
"# }\n",
|
||||
"\n",
|
||||
"# ]\n",
|
||||
"# }\n",
|
||||
"# headers = {\"Authorization\": token}\n",
|
||||
"# res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
|
||||
"# print(res.text)\n",
|
||||
"\n",
|
||||
"price_list = []\n",
|
||||
" \n",
|
||||
"def forecast_price():\n",
|
||||
@ -698,7 +681,7 @@
|
||||
" # 更新当日数据\n",
|
||||
" start(date)\n",
|
||||
" # 训练模型\n",
|
||||
" optimize_Model()\n",
|
||||
" # optimize_Model()\n",
|
||||
" # 预测&上传预测结果\n",
|
||||
" upload_data_to_system(token_push,start_date)\n",
|
||||
"\n"
|
||||
@ -717,15 +700,7 @@
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"运行中ing...\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"if __name__ == \"__main__\":\n",
|
||||
@ -734,16 +709,17 @@
|
||||
" while True:\n",
|
||||
" # 获取当前时间\n",
|
||||
" current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
|
||||
" try:\n",
|
||||
" # try:\n",
|
||||
" # 判断当前时间是否为执行任务的时间点\n",
|
||||
" if current_time == \"09:15:00\":\n",
|
||||
" print(\"执行定时任务\")\n",
|
||||
" main()\n",
|
||||
" elif current_time == \"20:00:00\":\n",
|
||||
" start_1()\n",
|
||||
" time.sleep(1)\n",
|
||||
" except:\n",
|
||||
" print(f\"{current_time}执行失败\")\n",
|
||||
" if current_time == \"09:15:00\":\n",
|
||||
" # if current_time == \"17:12:00\":\n",
|
||||
" print(\"执行定时任务\")\n",
|
||||
" main()\n",
|
||||
" elif current_time == \"20:00:00\":\n",
|
||||
" start_1()\n",
|
||||
" time.sleep(1)\n",
|
||||
" # except:\n",
|
||||
" # print(f\"{current_time}执行失败\")\n",
|
||||
"\n",
|
||||
" # 检测数据准确性, 需要检测放开\n",
|
||||
" # check_data(\"100028098|LISTING_PRICE\")\n",
|
||||
@ -754,10 +730,93 @@
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0NjY0NTcwMCwianRpIjoiODRhODFmYmZjYTUxNGM4ZGIzOGFiZTVlM2U0ZWFhYmUifQ.Vu5gbh0s1TMd3NE6T89l0h67TQaf3r_TDHvO_lZsIcs\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_2516\\268695125.py:602: 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",
|
||||
"20250507\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_2516\\268695125.py:602: 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",
|
||||
"获取到的数据项ID['YHQMXBB|C01100008|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|AMOUNT', 'C01100008|YEDAY_AMOUNT', '100028046|LISTING_PRICE', 'ICE_CL0_LAST_YEDAY_PRICE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0NjY0NTcwOCwianRpIjoiNDFjODI0ZjQ0MDIzNDZlZmI5NjY5NTcwZGE3MmZiYmYifQ.OUIHg0JlPstctzFKJ__8Qqo7oZuxedssnRJlUYXujtc\n",
|
||||
"获取20250507数据\n",
|
||||
"数据项查询参数search_data：\n",
|
||||
"{'data': {'date': '20250507', 'dataItemNoList': ['YHQMXBB|C01100008|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|AMOUNT', 'C01100008|YEDAY_AMOUNT', '100028046|LISTING_PRICE', 'ICE_CL0_LAST_YEDAY_PRICE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
|
||||
"数据项查询结果search_res：\n",
|
||||
"{\"confirmFlg\":false,\"data\":[{\"dataDate\":\"20250507\",\"dataItemNo\":\"91110000710926094P|C01100008|SUPPLY_MERE\",\"dataValue\":1300.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"91110000710932515R|C01100008|SUPPLY_MERE\"},{\"dataDate\":\"20250507\",\"dataItemNo\":\"91370321164425136B|C01100008|STRIKE_PRICE\",\"dataValue\":4750.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"91370321164425136B|C01100008|SUPPLY_MERE\",\"dataValue\":300.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"91370500674526498A|C01100008|SUPPLY_MERE\",\"dataValue\":175.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"91370521164880008P|C01100008|STRIKE_PRICE\",\"dataValue\":4805.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"C01100008|AUCTION_MAX_PRICE\",\"dataValue\":4770.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"C01100008|CORTED_VALUE\",\"dataValue\":4770.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"C01100008|OTHER|SUPPLY_MERE\",\"dataValue\":5000.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"C01100008|SUY_DED_DAP\",\"dataValue\":-50.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"ICE_CL0_LAST_YEDAY_PRICE\",\"dataValue\":62.04000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"SD|WJH|DEMANDS\",\"dataValue\":8500.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"SD|WJH|SALES_PRICE\",\"dataValue\":7830.00000000},{\"dataDate\":\"20250507\",\"dataItemNo\":\"YHQMXBB|C01100008|STRIKE_PRICE\",\"dataValue\":4770.00000000}],\"status\":true}\n",
|
||||
"数据项查询结果: [{'dataDate': '20250507', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250507', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250507', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 4750.0}, {'dataDate': '20250507', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 300.0}, {'dataDate': '20250507', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250507', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 4805.0}, {'dataDate': '20250507', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 4770.0}, {'dataDate': '20250507', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 4770.0}, {'dataDate': '20250507', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250507', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250507', 'dataItemNo': 'ICE_CL0_LAST_YEDAY_PRICE', 'dataValue': 62.04}, {'dataDate': '20250507', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250507', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 7830.0}, {'dataDate': '20250507', 'dataItemNo': 'YHQMXBB|C01100008|STRIKE_PRICE', 'dataValue': 4770.0}]\n",
|
||||
"{'dataDate': '20250507', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
|
||||
"添加的行： ['2025-05-07', 4770.0, 4770.0, 4770.0, '', '', '', 62.04, '', '', '', '', 4805.0, 4750.0, '', '', '', 1300.0, '', 175.0, 300.0, 5000.0, 8500.0, -50.0, '', '', '', '', '', 7830.0]\n",
|
||||
"更新 2025-05-07 数据\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_2516\\268695125.py:186: 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_2516\\268695125.py:188: 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_2516\\268695125.py:220: 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",
|
||||
"2025-05-07 4769.817871\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"{\"confirmFlg\":false,\"status\":true}\n",
|
||||
"预测值： 4769.82\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# start_date = datetime(2025, 4, 1)\n",
|
||||
"# end_date = datetime(2025, 5, 7)\n",
|
||||
"# start_date = datetime(2025, 5, 7)\n",
|
||||
"# end_date = datetime(2025, 5, 8)\n",
|
||||
"# token = get_head_auth()\n",
|
||||
"\n",
|
||||
"# while start_date < end_date:\n",
|
||||
@ -783,7 +842,7 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "base",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -797,7 +856,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
"version": "3.11.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Binary file not shown.
Binary file not shown.
@ -5,27 +5,17 @@
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:49: FutureWarning:\n",
|
||||
"\n",
|
||||
"The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
" <script type=\"text/javascript\">\n",
|
||||
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
|
||||
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
|
||||
" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.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",
|
||||
" 'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
|
||||
" }\n",
|
||||
" });\n",
|
||||
" require(['plotly'], function(Plotly) {\n",
|
||||
@ -43,7 +33,106 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"运行中ing\n"
|
||||
"运行中ing\n",
|
||||
"执行定时任务\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_22664\\2912906777.py:771: 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": [
|
||||
"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_22664\\2912906777.py:771: 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_22664\\2912906777.py:320: 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",
|
||||
"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: 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: 6.681e+05, tolerance: 4.960e+04\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using matplotlib backend: QtAgg\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": [
|
||||
"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\magics\\pylab.py:162: UserWarning:\n",
|
||||
"\n",
|
||||
"pylab import has clobbered these variables: ['__version__', 'datetime', 'plot', 'random']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Date\n",
|
||||
"2025-05-07 5427.172363\n",
|
||||
"Name: 日度预测价格, dtype: float32\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_22664\\2912906777.py:273: 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_22664\\2912906777.py:303: 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": [
|
||||
"{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250507', 'dataStatus': 'add', 'dataValue': 5427.17}]}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "KeyboardInterrupt",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[3], line 834\u001b[0m\n\u001b[0;32m 832\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m更新数据\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 833\u001b[0m start_1()\n\u001b[1;32m--> 834\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m1\u001b[39m)\n",
|
||||
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -246,6 +335,9 @@
|
||||
"def get_cur_time(date=''):\n",
|
||||
" if date == '':\n",
|
||||
" now = datetime.now()\n",
|
||||
" # 如果是字符串，尝试解析日期\n",
|
||||
" elif isinstance(date, str):\n",
|
||||
" now = datetime.strptime(date, '%Y-%m-%d')\n",
|
||||
" else:\n",
|
||||
" now = date\n",
|
||||
" year = now.year\n",
|
||||
@ -280,7 +372,7 @@
|
||||
" \"funcOperation\": \"新增\",\n",
|
||||
" \"data\": [\n",
|
||||
" {\"dataItemNo\": \"C01100047|FORECAST_PRICE\",\n",
|
||||
" \"dataDate\": get_cur_time(date)[0],\n",
|
||||
" \"dataDate\": date,\n",
|
||||
" \"dataStatus\": \"add\",\n",
|
||||
" \"dataValue\": datavalue\n",
|
||||
" }\n",
|
||||
@ -292,25 +384,7 @@
|
||||
" res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
|
||||
" print(res.text)\n",
|
||||
"\n",
|
||||
" \n",
|
||||
"# def upload_data_to_system(token):\n",
|
||||
"# data = {\n",
|
||||
"# \"funcModule\": \"数据表信息列表\",\n",
|
||||
"# \"funcOperation\": \"新增\",\n",
|
||||
"# \"data\": [\n",
|
||||
"# {\"dataItemNo\": \"C01100036|Forecast_ Price|ACN\",\n",
|
||||
"# \"dataDate\": '20230706',\n",
|
||||
"# \"dataStatus\": \"add\",\n",
|
||||
"# \"dataValue\": 3780.0\n",
|
||||
"# }\n",
|
||||
"\n",
|
||||
"# ]\n",
|
||||
"# }\n",
|
||||
"# headers = {\"Authorization\": token}\n",
|
||||
"# res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
|
||||
"# print(res.text)\n",
|
||||
"\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
"def forecast_price():\n",
|
||||
" # df_test = pd.read_csv('定价模型数据收集0212.csv')\n",
|
||||
@ -916,7 +990,7 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "base",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -930,7 +1004,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
"version": "3.11.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Binary file not shown.
236
models/xgboostmodels.py
Normal file
236
models/xgboostmodels.py
Normal file
@ -0,0 +1,236 @@
|
||||
from sklearn.metrics import mean_squared_error, r2_score
|
||||
from sklearn.feature_selection import SelectFromModel
|
||||
from sklearn.preprocessing import OrdinalEncoder
|
||||
from sklearn.impute import SimpleImputer
|
||||
from sklearn.model_selection import train_test_split
|
||||
import pandas as pd
|
||||
from sklearn.linear_model import Lasso
|
||||
from xgboost import XGBRegressor
|
||||
import statsmodels.api as sm
|
||||
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
|
||||
import warnings
|
||||
import pickle
|
||||
from sklearn.metrics import mean_squared_error
|
||||
from sklearn.metrics import r2_score
|
||||
import numpy as np
|
||||
from matplotlib import pyplot as plt
|
||||
import os
|
||||
from matplotlib import rcParams
|
||||
rcParams['font.sans-serif'] = ['SimHei'] # Windows系统自带的中文字体
|
||||
rcParams['axes.unicode_minus'] = False # 解决负号显示问题
|
||||
|
||||
pd.set_option('display.max_rows', 40)
|
||||
pd.set_option('display.max_columns', 40)
|
||||
|
||||
|
||||
class xgboostmodels():
|
||||
def __init__(self, df, steps=3):
|
||||
self.df = df
|
||||
self.steps = steps
|
||||
self.features = []
|
||||
|
||||
def optimize_Model(self, df):
|
||||
# 生成滞后特征（示例使用3期滞后）
|
||||
steps = self.steps
|
||||
for i in range(1, steps+1):
|
||||
df[f'lag_{i}'] = df['y'].shift(i)
|
||||
|
||||
# 生成多步预测目标（示例预测未来3步）
|
||||
forecast_horizon = steps
|
||||
for i in range(forecast_horizon):
|
||||
df[f'target_{i+1}'] = df['y'].shift(-(i+1))
|
||||
|
||||
df = df.dropna() # 删除包含NaN的行
|
||||
|
||||
# 更新特征和目标变量
|
||||
features = [col for col in df.columns if col.startswith('lag')]
|
||||
targets = [col for col in df.columns if col.startswith('target')]
|
||||
|
||||
X = df[features]
|
||||
y = df[targets]
|
||||
|
||||
# 按时间顺序分割数据集（时间序列不能随机分割）
|
||||
split = int(0.8 * len(df))
|
||||
X_train, x_test = X.iloc[:split], X.iloc[split:]
|
||||
y_train, y_test = y.iloc[:split], y.iloc[split:]
|
||||
|
||||
# 修改模型为多输出回归
|
||||
from sklearn.multioutput import MultiOutputRegressor
|
||||
base_model = XGBRegressor(random_state=0)
|
||||
estimator = MultiOutputRegressor(base_model) # 保持多输出包装器
|
||||
|
||||
# 模型参数网格需要调整为适用于多输出回归器
|
||||
parameters = {
|
||||
'estimator__max_depth': range(2, 11, 2), # 注意参数前缀
|
||||
'estimator__n_estimators': range(50, 101, 10),
|
||||
'estimator__learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]
|
||||
}
|
||||
|
||||
grid_search_XGB = GridSearchCV(
|
||||
estimator=estimator,
|
||||
param_grid=parameters,
|
||||
cv=3,
|
||||
verbose=True,
|
||||
scoring='neg_mean_squared_error' # 使用更适合时序的评估指标
|
||||
)
|
||||
|
||||
grid_search_XGB.fit(X_train, y_train)
|
||||
|
||||
# 优化后的结果展示
|
||||
print("最佳参数组合:")
|
||||
best_params = {}
|
||||
for param, value in grid_search_XGB.best_params_.items():
|
||||
best_params[param.split('__')[1]] = value
|
||||
print(f"{param}: {value}")
|
||||
|
||||
best_params_df = pd.DataFrame(
|
||||
[best_params], index=["最佳参数组合"])
|
||||
y_pred = grid_search_XGB.predict(x_test)
|
||||
|
||||
# 多步预测评估指标
|
||||
evaluation_results = []
|
||||
for step in range(forecast_horizon):
|
||||
mse = mean_squared_error(y_test.iloc[:, step], y_pred[:, step])
|
||||
rmse = np.sqrt(mse)
|
||||
r2 = r2_score(y_test.iloc[:, step], y_pred[:, step])
|
||||
|
||||
evaluation_results.append({
|
||||
'预测步长': step+1,
|
||||
'均方根误差(RMSE)': rmse,
|
||||
'R²分数': r2
|
||||
})
|
||||
|
||||
# 生成多步预测结果对比表
|
||||
results_df = pd.DataFrame(evaluation_results).set_index('预测步长')
|
||||
print("\n多步预测性能评估:")
|
||||
print(results_df)
|
||||
|
||||
# 保存完整模型（包含多输出包装器）
|
||||
Pkl_Filename = "report\日度价格预测_最佳模型.pkl"
|
||||
with open(Pkl_Filename, 'wb') as file:
|
||||
pickle.dump(grid_search_XGB.best_estimator_, file)
|
||||
|
||||
return results_df, best_params_df
|
||||
|
||||
def forecast_price(self, df):
|
||||
steps = self.steps
|
||||
import joblib
|
||||
Best_model_DalyLGPrice = joblib.load(
|
||||
"report\日度价格预测_最佳模型.pkl")
|
||||
df = df.copy()
|
||||
df.set_index('ds', inplace=True)
|
||||
|
||||
# 生成滞后特征（与训练时保持一致）
|
||||
for i in range(1, steps+1):
|
||||
df[f'lag_{i}'] = df['y'].shift(i)
|
||||
|
||||
# 获取特征列（保持与训练时相同的特征顺序）
|
||||
features = [f'lag_{i}' for i in range(1, steps+1)]
|
||||
|
||||
# 初始化预测序列（保留最后3个已知值作为初始输入）
|
||||
current_values = df[features][-1:].values # 初始形状 (1,3)
|
||||
|
||||
predictions = Best_model_DalyLGPrice.predict(current_values)[0]
|
||||
|
||||
# 生成带日期的预测结果
|
||||
last_date = df.index[-1]
|
||||
date_range = pd.date_range(
|
||||
start=last_date + pd.Timedelta(days=1),
|
||||
periods=steps
|
||||
)
|
||||
|
||||
# 保存预测结果到DataFrame
|
||||
forecast_series = pd.Series(
|
||||
predictions, index=date_range.strftime('%Y-%m-%d'), name='预测值')
|
||||
|
||||
# 拼接原始数据和预测结果
|
||||
df = pd.concat([df[['y']], forecast_series], axis=0)
|
||||
# 绘制预测结果
|
||||
plt.figure(figsize=(12, 6))
|
||||
n_points = min(len(df), 30) # 自动适配数据量
|
||||
plt.plot(df.index[-n_points:], df['y'].iloc[-n_points:],
|
||||
label=f'原始数据(近{n_points}天)', marker='o', markersize=4)
|
||||
|
||||
plt.plot(forecast_series.index, forecast_series.values,
|
||||
label=f'预测值 (未来{steps}天)', linestyle='--', marker='x')
|
||||
|
||||
plt.axvline(x=last_date, color='red', linestyle=':',
|
||||
label='当前日期分界线', alpha=0.7)
|
||||
plt.title('价格预测趋势')
|
||||
plt.xlabel('日期')
|
||||
plt.ylabel('价格')
|
||||
plt.legend()
|
||||
plt.grid(True)
|
||||
|
||||
# 自动旋转日期标签
|
||||
plt.gcf().autofmt_xdate()
|
||||
|
||||
# 确保保存目录存在
|
||||
os.makedirs("report", exist_ok=True)
|
||||
plt.savefig("report/价格预测.png", dpi=300, bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
return forecast_series
|
||||
|
||||
def generate_xgboost_report(self, model_results, best_params_df, forecast_series,
|
||||
save_path="report/xgboost_report.md"):
|
||||
"""
|
||||
生成XGBoost模型报告
|
||||
参数:
|
||||
model_results: 模型对比结果DataFrame
|
||||
evaluation_df: 多步评估结果DataFrame
|
||||
forecast_series: 预测结果Series
|
||||
save_path: 报告保存路径
|
||||
"""
|
||||
# 构建Markdown内容
|
||||
md_content = f"""
|
||||
# XGBoost价格预测分析报告 <span style="color:gray; font-size:0.8em">{pd.Timestamp.now().strftime('%Y-%m-%d')}</span>
|
||||
|
||||
## 一、模型实现
|
||||
|
||||
### 1. 特征工程
|
||||
- 使用{len(model_results)}期历史滞后特征
|
||||
- 预测未来{len(model_results)}个时间步长
|
||||
- 数据集分割比例：80% 训练集 / 20% 测试集
|
||||
|
||||
### 2. 模型配置
|
||||
|
||||
{best_params_df.to_markdown()}
|
||||
|
||||
## 二、性能评估
|
||||
### 多步预测误差分析
|
||||
{model_results.to_markdown()}
|
||||
|
||||
## 三、预测结果
|
||||
### 未来{len(forecast_series)}日价格预测
|
||||
|
||||
{forecast_series.to_markdown()}
|
||||
|
||||
"""
|
||||
|
||||
# 保存报告
|
||||
os.makedirs(os.path.dirname(save_path), exist_ok=True)
|
||||
with open(save_path, 'w', encoding='utf-8') as f:
|
||||
f.write(md_content)
|
||||
|
||||
print(f"报告已生成至：{os.path.abspath(save_path)}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
df = pd.read_csv(
|
||||
"D:\code\PriceForecast-svn\yuanyoudataset\指标数据.csv")
|
||||
df['ds'] = pd.to_datetime(df['ds'])
|
||||
xg = xgboostmodels(df=df, steps=5)
|
||||
model_results, best_params_df = xg.optimize_Model(df=df)
|
||||
forecast_series = xg.forecast_price(df)
|
||||
xg.generate_xgboost_report(
|
||||
model_results=model_results,
|
||||
best_params_df=best_params_df,
|
||||
forecast_series=forecast_series
|
||||
)
|
||||
40
report/xgboost_report.md
Normal file
40
report/xgboost_report.md
Normal file
@ -0,0 +1,40 @@
|
||||
# XGBoost 价格预测分析报告 <span style="color:gray; font-size:0.8em">2025-05-08</span>
|
||||
|
||||
## 一、模型实现
|
||||
|
||||
### 1. 特征工程
|
||||
|
||||
- 使用 5 期历史滞后特征
|
||||
- 预测未来 10 个时间步长
|
||||
- 数据集分割比例：80% 训练集 / 20% 测试集
|
||||
|
||||
### 2. 模型配置
|
||||
|
||||
| | learning_rate | max_depth | n_estimators |
|
||||
| :----------- | ------------: | --------: | -----------: |
|
||||
| 最佳参数组合 | 1 | 4 | 100 |
|
||||
|
||||
## 二、性能评估
|
||||
|
||||
### 多步预测误差分析
|
||||
|
||||
| 预测步长 | 均方根误差(RMSE) | R² 分数 |
|
||||
| -------: | ---------------: | -------: |
|
||||
| 1 | 2.30044 | 0.828193 |
|
||||
| 2 | 2.74815 | 0.756595 |
|
||||
| 3 | 3.16346 | 0.679378 |
|
||||
| 4 | 3.62202 | 0.58126 |
|
||||
| 5 | 3.77657 | 0.546517 |
|
||||
|
||||
## 三、预测结果
|
||||
|
||||
### 未来 5 日价格预测
|
||||
|
||||
|
||||
| | 预测值 |
|
||||
|:--------------------|---------:|
|
||||
| 2025-03-14 00:00:00 | 68.8125 |
|
||||
| 2025-03-15 00:00:00 | 68.6643 |
|
||||
| 2025-03-16 00:00:00 | 69.283 |
|
||||
| 2025-03-17 00:00:00 | 71.7288 |
|
||||
| 2025-03-18 00:00:00 | 68.8356 |
|
||||
BIN
report/日度价格预测_最佳模型.pkl
Normal file
BIN
report/日度价格预测_最佳模型.pkl
Normal file
Binary file not shown.
BIN
日度价格预测_最佳模型.pkl
Normal file
BIN
日度价格预测_最佳模型.pkl
Normal file
Binary file not shown.
Loading…
Reference in New Issue
Block a user