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石油苯数据更新，聚烯烃配置，聚烯烃周度配置，聚烯烃主函数

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workpc 2025-02-21 11:17:10 +08:00
parent 4f2a888dff
commit 38fbd7c810
13 changed files with 1251 additions and 160 deletions
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6
.gitignore vendored
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@ -10,11 +10,7 @@ __pycache__/
# Distribution / packaging
.Python
build/
dataset/
yuanyoudataset/
yuanyouzhoududataset/
yuanyouyuedudataset/
juxitingdataset/
*dataset/
logs/
develop-eggs/
dist/

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aisenzhecode/石油苯/日度价格预测_最佳模型.pkl Normal file
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aisenzhecode/石油苯/纯苯价格预测-自定义日期ytj.ipynb
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@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 6,
"metadata": {},
"outputs": [
{
@ -706,27 +706,28 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20241017\n"
"20250201\n",
"{'dataDate': '20250201', 'dataItemNo': 'C01100047|TURNOVER'}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:280: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 4.602e+05, tolerance: 3.845e+04\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: 5.321e+05, tolerance: 4.074e+04\n",
"\n"
]
},
@ -734,7 +735,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Using matplotlib backend: QtAgg\n",
"Using matplotlib backend: <object object at 0x0000028F59DBAF30>\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"
@ -749,11 +750,11 @@
"pylab import has clobbered these variables: ['datetime', '__version__', 'plot', 'random']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n",
"\n",
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:233: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:263: FutureWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:263: 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"
@ -764,24 +765,25 @@
"output_type": "stream",
"text": [
"Date\n",
"2024-10-17 7541.753418\n",
"2025-02-01 7738.433105\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': '20241017', 'dataStatus': 'add', 'dataValue': 7541.75}]}\n",
"{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250201', 'dataStatus': 'add', 'dataValue': 7738.43}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20241018\n"
"20250202\n",
"{'dataDate': '20250202', 'dataItemNo': 'C01100047|TURNOVER'}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:280: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 4.716e+05, tolerance: 3.895e+04\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: 5.506e+05, tolerance: 4.089e+04\n",
"\n"
]
},
@ -799,11 +801,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:233: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:263: FutureWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:263: 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"
@ -814,24 +816,25 @@
"output_type": "stream",
"text": [
"Date\n",
"2024-10-18 7399.281738\n",
"2025-02-02 7700.021484\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': '20241018', 'dataStatus': 'add', 'dataValue': 7399.28}]}\n",
"{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250202', 'dataStatus': 'add', 'dataValue': 7700.02}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20241019\n"
"20250203\n",
"{'dataDate': '20250203', 'dataItemNo': 'C01100047|TURNOVER'}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:280: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 4.669e+05, tolerance: 3.913e+04\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: 5.696e+05, tolerance: 4.096e+04\n",
"\n"
]
},
@ -849,11 +852,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:233: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:263: FutureWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:263: 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"
@ -864,24 +867,25 @@
"output_type": "stream",
"text": [
"Date\n",
"2024-10-19 7404.584473\n",
"2025-02-03 7693.463379\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': '20241019', 'dataStatus': 'add', 'dataValue': 7404.58}]}\n",
"{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250203', 'dataStatus': 'add', 'dataValue': 7693.46}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20241020\n"
"20250204\n",
"{'dataDate': '20250204', 'dataItemNo': 'C01100047|TURNOVER'}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:280: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 4.833e+05, tolerance: 3.773e+04\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: 5.780e+05, tolerance: 4.200e+04\n",
"\n"
]
},
@ -899,11 +903,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:233: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:263: FutureWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:263: 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"
@ -914,24 +918,25 @@
"output_type": "stream",
"text": [
"Date\n",
"2024-10-20 7375.245605\n",
"2025-02-04 7798.116211\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': '20241020', 'dataStatus': 'add', 'dataValue': 7375.25}]}\n",
"{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250204', 'dataStatus': 'add', 'dataValue': 7798.12}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20241021\n"
"20250205\n",
"{'dataDate': '20250205', 'dataItemNo': 'C01100047|TURNOVER'}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:280: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 4.912e+05, tolerance: 3.684e+04\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: 5.551e+05, tolerance: 4.144e+04\n",
"\n"
]
},
@ -949,11 +954,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:233: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:263: FutureWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:263: 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"
@ -964,24 +969,24 @@
"output_type": "stream",
"text": [
"Date\n",
"2024-10-21 7272.15332\n",
"2025-02-05 7865.974609\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': '20241021', 'dataStatus': 'add', 'dataValue': 7272.15}]}\n",
"{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250205', 'dataStatus': 'add', 'dataValue': 7865.97}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20241022\n"
"20250206\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:280: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 4.991e+05, tolerance: 3.700e+04\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: 5.553e+05, tolerance: 4.144e+04\n",
"\n"
]
},
@ -999,11 +1004,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:233: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:263: FutureWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:263: 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"
@ -1014,24 +1019,24 @@
"output_type": "stream",
"text": [
"Date\n",
"2024-10-22 7265.592773\n",
"2025-02-06 7896.265137\n",
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"{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20241022', 'dataStatus': 'add', 'dataValue': 7265.59}]}\n",
"{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250206', 'dataStatus': 'add', 'dataValue': 7896.27}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20241023\n"
"20250207\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:280: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 4.866e+05, tolerance: 3.682e+04\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: 5.955e+05, tolerance: 4.168e+04\n",
"\n"
]
},
@ -1049,11 +1054,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:233: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:263: FutureWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:263: 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"
@ -1064,24 +1069,24 @@
"output_type": "stream",
"text": [
"Date\n",
"2024-10-23 7314.694336\n",
"2025-02-07 7841.537109\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': '20241023', 'dataStatus': 'add', 'dataValue': 7314.69}]}\n",
"{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250207', 'dataStatus': 'add', 'dataValue': 7841.54}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20241024\n"
"20250208\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:280: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 4.886e+05, tolerance: 3.690e+04\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: 5.733e+05, tolerance: 4.197e+04\n",
"\n"
]
},
@ -1099,11 +1104,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:233: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:263: FutureWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:263: 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"
@ -1114,24 +1119,24 @@
"output_type": "stream",
"text": [
"Date\n",
"2024-10-24 7340.938477\n",
"2025-02-08 7814.474609\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': '20241024', 'dataStatus': 'add', 'dataValue': 7340.94}]}\n",
"{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250208', 'dataStatus': 'add', 'dataValue': 7814.47}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20241025\n"
"20250209\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:280: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 4.843e+05, tolerance: 3.691e+04\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: 5.675e+05, tolerance: 4.062e+04\n",
"\n"
]
},
@ -1149,11 +1154,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:233: UserWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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\\Hello\\AppData\\Local\\Temp\\ipykernel_13372\\3095856616.py:263: FutureWarning:\n",
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:263: 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"
@ -1164,9 +1169,463 @@
"output_type": "stream",
"text": [
"Date\n",
"2024-10-25 7299.914062\n",
"2025-02-09 7832.284668\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': '20241025', 'dataStatus': 'add', 'dataValue': 7299.91}]}\n",
"{'funcModule': '数据表信息列表', 'funcOperation': '新增', 'data': [{'dataItemNo': 'C01100047|FORECAST_PRICE', 'dataDate': '20250209', 'dataStatus': 'add', 'dataValue': 7832.28}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20250210\n",
"{'dataDate': '20250210', 'dataItemNo': 'C01100047|TURNOVER'}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 5.531e+05, tolerance: 4.103e+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": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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",
"2025-02-10 7917.837891\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': '20250210', 'dataStatus': 'add', 'dataValue': 7917.84}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20250211\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 5.307e+05, tolerance: 4.073e+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": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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",
"2025-02-11 7919.563965\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': '20250211', 'dataStatus': 'add', 'dataValue': 7919.56}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20250212\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 5.644e+05, tolerance: 4.133e+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": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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",
"2025-02-12 7902.145508\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': '20250212', 'dataStatus': 'add', 'dataValue': 7902.15}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20250213\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 5.711e+05, tolerance: 4.105e+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": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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",
"2025-02-13 8001.087891\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': '20250213', 'dataStatus': 'add', 'dataValue': 8001.09}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20250214\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 5.949e+05, tolerance: 4.129e+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": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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",
"2025-02-14 8032.705566\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': '20250214', 'dataStatus': 'add', 'dataValue': 8032.71}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20250215\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 5.966e+05, tolerance: 4.129e+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": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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",
"2025-02-15 8040.26709\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': '20250215', 'dataStatus': 'add', 'dataValue': 8040.27}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20250216\n",
"{'dataDate': '20250216', 'dataItemNo': 'C01100047|TURNOVER'}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 5.442e+05, tolerance: 4.138e+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": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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",
"2025-02-16 8044.537109\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': '20250216', 'dataStatus': 'add', 'dataValue': 8044.54}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20250217\n",
"{'dataDate': '20250217', 'dataItemNo': 'C01100047|TURNOVER'}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 5.555e+05, tolerance: 4.072e+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": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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",
"2025-02-17 7998.32373\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': '20250217', 'dataStatus': 'add', 'dataValue': 7998.32}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"20250218\n",
"{'dataDate': '20250218', 'dataItemNo': 'C01100047|TURNOVER'}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:280: 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: 5.680e+05, tolerance: 4.121e+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": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_21000\\3095856616.py:233: 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_21000\\3095856616.py:263: 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",
"2025-02-18 7988.078125\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': '20250218', 'dataStatus': 'add', 'dataValue': 7988.08}]}\n",
"{\"confirmFlg\":false,\"status\":true}\n"
]
}
@ -1174,16 +1633,23 @@
"source": [
"from datetime import datetime, timedelta\n",
"\n",
"start_date = datetime(2024, 10, 17)\n",
"end_date = datetime(2024, 10, 26)\n",
"start_date = datetime(2025, 2, 1)\n",
"end_date = datetime(2025, 2, 19)\n",
"\n",
"while start_date < end_date:\n",
" print(start_date.strftime('%Y%m%d'))\n",
" start(start_date)\n",
" # time.sleep(1)\n",
" # time.sleep(2)\n",
" # start_1(start_date)\n",
" start_date += timedelta(days=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

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13
config_jingbo.py
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@ -95,9 +95,9 @@ ClassifyId = 1214
server_host = '192.168.100.53'
login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave" # 上传报告
upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save" # 停更预警
query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos" # 查询数据项编码
login_data = {
"data": {
@ -162,8 +162,8 @@ table_name = 'v_tbl_crude_oil_warning'
### 开关
is_train = False # 是否训练
is_debug = False # 是否调试
is_eta = True # 是否使用eta接口
is_debug = True # 是否调试
is_eta = False # 是否使用eta接口
is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
is_timefurture = True # 是否使用时间特征
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
@ -185,7 +185,7 @@ print("数据库连接成功",host,dbname,dbusername)
# 数据截取日期
start_year = 2020 # 数据开始年份
end_time = '' # 数据截取日期
end_time = '' # 数据截取日期 格式为 2024-01-01
freq = 'B' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
delweekenday = True if freq == 'B' else False # 是否删除周末数据
is_corr = False # 特征是否参与滞后领先提升相关系数
@ -195,6 +195,7 @@ if add_kdj and is_edbnamelist:
### 模型参数
y = 'Brent连1合约价格' # 原油指标数据的目标变量 Brent连1合约价格 Brent活跃合约
# y = 'Brent连1合约价格' # 原油指标数据的目标变量 Brent连1合约价格 Brent活跃合约
horizon =5 # 预测的步长
input_size = 40 # 输入序列长度
train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数

8
config_juxiting.py
View File

@ -157,6 +157,7 @@ upload_data = {
"funcModule":'研究报告信息',
"funcOperation":'上传聚烯烃PP价格预测报告',
"data":{
"groupNo": "000127",
"ownerAccount":'arui', #报告所属用户账号
"reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
"fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #文件名称
@ -174,6 +175,7 @@ warning_data = {
"funcModule":'原油特征停更预警',
"funcOperation":'原油特征停更预警',
"data":{
"groupNo": "000127",
'WARNING_TYPE_NAME':'特征数据停更预警',
'WARNING_CONTENT':'',
'WARNING_DATE':''
@ -202,16 +204,16 @@ table_name = 'v_tbl_crude_oil_warning'
### 开关
is_train = False # 是否训练
is_debug = False # 是否调试
is_debug = True # 是否调试
is_eta = False # 是否使用eta接口
is_market = False # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
is_timefurture = True # 是否使用时间特征
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
is_edbcode = False # 特征使用edbcoding列表中的
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
is_update_eta = True # 预测结果上传到eta
is_update_eta = False # 预测结果上传到eta
is_update_report = True # 是否上传报告
is_update_warning_data = False # 是否上传预警数据
is_update_warning_data = True # 是否上传预警数据
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
is_del_tow_month = True # 是否删除两个月不更新的特征

318
config_juxiting_zhoudu.py Normal file
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@ -0,0 +1,318 @@
import logging
import os
import logging.handlers
import datetime
from lib.tools import MySQLDB,SQLiteHandler
# eta 接口token
APPID = "XNLDvxZHHugj7wJ7"
SECRET = "iSeU4s6cKKBVbt94htVY1p0sqUMqb2xa"
# eta 接口url
sourcelisturl = 'http://10.189.2.78:8108/v1/edb/source/list'
classifylisturl = 'http://10.189.2.78:8108/v1/edb/classify/list?ClassifyType='
uniquecodedataurl = 'http://10.189.2.78:8108/v1/edb/data?UniqueCode=4991c37becba464609b409909fe4d992&StartDate=2024-02-01'
classifyidlisturl = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId='
edbcodedataurl = 'http://10.189.2.78:8108/v1/edb/data?EdbCode='
edbdatapushurl = 'http://10.189.2.78:8108/v1/edb/push'
edbdeleteurl = 'http://10.189.2.78:8108/v1/edb/business/edb/del'
edbbusinessurl = 'http://10.189.2.78:8108/v1/edb/business/data/del'
edbcodelist = ['ID01385938','lmcads03 lme comdty',
'GC1 COMB Comdty',
'C2404171822',
'dxy curncy',
'S5443199 ',
'S5479800',
'S5443108',
'H7358586',
'LC3FM1 INDEX',
'CNY REGN Curncy',
's0105897',
'M0067419',
'M0066351',
'S0266372',
'S0266438',
'S0266506',
'ID01384463']
# 临时写死用指定的列,与上面的edbcode对应，后面更改
edbnamelist = [
'ds','y',
'LME铜价',
'黄金连1合约',
'Brent-WTI',
'美元指数',
'甲醇鲁南价格',
'甲醇太仓港口价格',
'山东丙烯主流价',
'丙烷（山东）',
'FEI丙烷 M1',
'在岸人民币汇率',
'南华工业品指数',
'PVC期货主力',
'PE期货收盘价',
'PP连续-1月',
'PP连续-5月',
'PP连续-9月',
'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）'
]
edbcodenamedict = {
'ID01385938':'PP：拉丝：1102K：市场价：青州：国家能源宁煤（日）',
'ID01384463':'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）',
'lmcads03 lme comdty':'LME铜价',
'GC1 COMB Comdty':'黄金连1合约',
'C2404171822':'Brent-WTI',
'dxy curncy':'美元指数',
'S5443199 ':'甲醇鲁南价格',
'S5479800':'甲醇太仓港口价格',
'S5443108':'山东丙烯主流价',
'H7358586':'丙烷（山东）',
'LC3FM1 INDEX':'FEI丙烷 M1',
'CNY REGN Curncy':'在岸人民币汇率',
's0105897':'南华工业品指数',
'M0067419':'PVC期货主力',
'M0066351':'PE期货收盘价',
'S0266372':'PP连续-1月',
'S0266438':'PP连续-5月',
'S0266506':'PP连续-9月',
}
# eta自有数据指标编码
modelsindex = {
'NHITS': 'SELF0000077',
'Informer':'SELF0000078',
'LSTM':'SELF0000079',
'iTransformer':'SELF0000080',
'TSMixer':'SELF0000081',
'TSMixerx':'SELF0000082',
'PatchTST':'SELF0000083',
'RNN':'SELF0000084',
'GRU':'SELF0000085',
'TCN':'SELF0000086',
'BiTCN':'SELF0000087',
'DilatedRNN':'SELF0000088',
'MLP':'SELF0000089',
'DLinear':'SELF0000090',
'NLinear':'SELF0000091',
'TFT':'SELF0000092',
'FEDformer':'SELF0000093',
'StemGNN':'SELF0000094',
'MLPMultivariate':'SELF0000095',
'TiDE':'SELF0000096',
'DeepNPTS':'SELF0000097'
}
# eta 上传预测结果的请求体，后面发起请求的时候更改 model datalist 数据
data = {
"IndexCode": "",
"IndexName": "价格预测模型",
"Unit": "无",
"Frequency": "日度",
"SourceName": f"价格预测",
"Remark": 'ddd',
"DataList": [
{
"Date": "2024-05-02",
"Value": 333444
}
]
}
# eta 分类
# level：3才可以获取到数据，所以需要人工把能源化工下所有的level3级都找到
# url = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId=1214'
#ParentId ":1160, 能源化工
# ClassifyId ":1214,原油
#ParentId ":1214,"，就是原油下所有的数据。
ClassifyId = 1161
############################################################################################################### 变量定义--测试环境
server_host = '192.168.100.53'
login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
login_data = {
"data": {
"account": "api_test",
# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
"terminal": "API"
},
"funcModule": "API",
"funcOperation": "获取token"
}
upload_data = {
"funcModule":'研究报告信息',
"funcOperation":'上传聚烯烃PP价格预测报告',
"data":{
"ownerAccount":'arui', #报告所属用户账号
"reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
"fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #文件名称
"fileBase64": '' ,#文件内容base64
"categoryNo":'yyjgycbg', # 研究报告分类编码
"smartBusinessClassCode":'JXTJGYCBG', #分析报告分类编码
"reportEmployeeCode":"E40116", # 报告人
"reportDeptCode" :"D0044" ,# 报告部门
"productGroupCode":"RAW_MATERIAL" # 商品分类
}
}
warning_data = {
"funcModule":'原油特征停更预警',
"funcOperation":'原油特征停更预警',
"data":{
'WARNING_TYPE_NAME':'特征数据停更预警',
'WARNING_CONTENT':'',
'WARNING_DATE':''
}
}
query_data_list_item_nos_data = {
"funcModule": "数据项",
"funcOperation": "查询",
"data": {
"dateStart":"20200101",
"dateEnd":"20241231",
"dataItemNoList":["Brentzdj","Brentzgj"] # 数据项编码,代表 brent最低价和最高价
}
}
# 北京环境数据库
host = '192.168.101.27'
port = 3306
dbusername ='root'
password = '123456'
dbname = 'jingbo_test'
table_name = 'v_tbl_crude_oil_warning'
### 开关
is_train = False # 是否训练
is_debug = False # 是否调试
is_eta = False # 是否使用eta接口
is_market = False # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
is_timefurture = True # 是否使用时间特征
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
is_edbcode = False # 特征使用edbcoding列表中的
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
is_update_eta = True # 预测结果上传到eta
is_update_report = True # 是否上传报告
is_update_warning_data = False # 是否上传预警数据
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
is_del_tow_month = True # 是否删除两个月不更新的特征
# 连接到数据库
db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
db_mysql.connect()
print("数据库连接成功",host,dbname,dbusername)
# 数据截取日期
start_year = 2020 # 数据开始年份
end_time = '2025-01-27' # 数据截取日期
freq = 'W' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
delweekenday = True if freq == 'B' else False # 是否删除周末数据
is_corr = False # 特征是否参与滞后领先提升相关系数
add_kdj = False # 是否添加kdj指标
if add_kdj and is_edbnamelist:
edbnamelist = edbnamelist+['K','D','J']
### 模型参数
y = 'AVG-金能大唐久泰青州'
avg_cols = [
'PP：拉丝：1102K：出厂价：青州：国家能源宁煤（日）',
'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）',
'PP：拉丝：L5E89：出厂价：河北、鲁北：大唐内蒙多伦（日）',
'PP：拉丝：HP550J：市场价：青岛：金能化学（日）'
]
offsite = 80
offsite_col = ['PP：拉丝：HP550J：市场价：青岛：金能化学（日）']
horizon =1 # 预测的步长
input_size = 7 # 输入序列长度
train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数
val_check_steps = 30 # 评估频率
early_stop_patience_steps = 5 # 早停的耐心步数
# --- 交叉验证用的参数
test_size = 200 # 测试集大小，定义100，后面使用的时候重新赋值
val_size = test_size # 验证集大小,同测试集大小
### 特征筛选用到的参数
k = 100 # 特征筛选数量，如果是0或者值比特征数量大，代表全部特征
corr_threshold = 0.6 # 相关性大于0.6的特征
rote = 0.06 # 绘图上下界阈值
### 计算准确率
weight_dict = [0.4,0.15,0.1,0.1,0.25] # 权重
### 文件
data_set = 'PP指标数据.xlsx' # 数据集文件
dataset = 'juxitingzhududataset' # 数据集文件夹
# 数据库名称
db_name = os.path.join(dataset,'jbsh_juxiting.db')
sqlitedb = SQLiteHandler(db_name)
sqlitedb.connect()
settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
# 获取日期时间
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # 获取当前日期时间
now = datetime.datetime.now().strftime('%Y-%m-%d') # 获取当前日期时间
reportname = f'PP大模型预测报告--{end_time}.pdf' # 报告文件名
reportname = reportname.replace(':', '-') # 替换冒号
if end_time == '':
end_time = now
### 邮件配置
username='1321340118@qq.com'
passwd='wgczgyhtyyyyjghi'
# recv=['liurui_test@163.com','52585119@qq.com']
recv=['liurui_test@163.com']
# recv=['liurui_test@163.com']
title='reportname'
content=y+'预测报告请看附件'
file=os.path.join(dataset,'reportname')
# file=os.path.join(dataset,'14-7-50--100-原油指标数据.xlsx-Brent连1合约价格--20240731175936-预测报告.pdf')
ssl=True
### 日志配置
# 创建日志目录（如果不存在）
log_dir = 'logs'
if not os.path.exists(log_dir):
os.makedirs(log_dir)
# 配置日志记录器
logger = logging.getLogger('my_logger')
logger.setLevel(logging.INFO)
# 配置文件处理器，将日志记录到文件
file_handler = logging.handlers.RotatingFileHandler(os.path.join(log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
file_handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
# 配置控制台处理器，将日志打印到控制台
console_handler = logging.StreamHandler()
console_handler.setFormatter(logging.Formatter('%(message)s'))
# 将处理器添加到日志记录器
logger.addHandler(file_handler)
logger.addHandler(console_handler)
# logger.info('当前配置：'+settings)

8
lib/dataread.py
View File

@ -44,8 +44,9 @@ plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
# from config_jingbo import *
# from config_jingbo_zhoudu import *
# from config_yongan import *
# from config_juxiting import *
from config_juxiting_pro import *
from config_juxiting import *
# from config_juxiting_zhoudu import *
# from config_juxiting_pro import *
@ -259,12 +260,13 @@ def upload_warning_info(df_count):
try:
# 获取当前日期
warning_date = datetime.datetime.now().strftime('%Y-%m-%d')
warning_date2 = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
# 构建预警内容
content = f'{warning_date}有{df_count}个停更'
# 更新预警数据中的日期和内容
warning_data['data']['WARNING_DATE'] = warning_date
warning_data['data']['WARNING_DATE'] = warning_date2
warning_data['data']['WARNING_CONTENT'] = content
# 调用 upload_warning_data 函数上传预警数据

92
main_juxiting.py
View File

@ -188,33 +188,35 @@ def predict_main():
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
try:
if is_weekday:
# if True:
logger.info('今天是周一，发送特征预警')
# 上传预警信息到数据库
warning_data_df = df_zhibiaoliebiao.copy()
warning_data_df = warning_data_df[warning_data_df['停更周期']> 3 ][['指标名称', '指标id', '频度','更新周期','指标来源','最后更新时间','停更周期']]
# 重命名列名
warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
from sqlalchemy import create_engine
import urllib
global password
if '@' in password:
password = urllib.parse.quote_plus(password)
# if is_weekday:
if True:
# logger.info('今天是周一，发送特征预警')
# # 上传预警信息到数据库
# warning_data_df = df_zhibiaoliebiao.copy()
# warning_data_df = warning_data_df[warning_data_df['停更周期']> 3 ][['指标名称', '指标id', '频度','更新周期','指标来源','最后更新时间','停更周期']]
# # 重命名列名
# warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
# from sqlalchemy import create_engine
# import urllib
# global password
# if '@' in password:
# password = urllib.parse.quote_plus(password)
engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
warning_data_df['TENANT_CODE'] = 'T0004'
# 插入数据之前查询表数据然后新增id列
existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
if not existing_data.empty:
max_id = existing_data['ID'].astype(int).max()
warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
else:
warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
warning_data_df.to_sql(table_name, con=engine, if_exists='append', index=False)
# engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
# warning_data_df['WARNING_DATE'] = datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
# warning_data_df['TENANT_CODE'] = 'T0004'
# # 插入数据之前查询表数据然后新增id列
# existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
# if not existing_data.empty:
# max_id = existing_data['ID'].astype(int).max()
# warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
# else:
# warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
# warning_data_df.to_sql(table_name, con=engine, if_exists='append', index=False)
# if is_update_warning_data:
# upload_warning_info(len(warning_data_df))
if is_update_warning_data:
upload_warning_info(len(warning_data_df))
upload_warning_info(10)
except:
logger.info('上传预警信息到数据库失败')
@ -226,26 +228,26 @@ def predict_main():
row, col = df.shape
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
ex_Model_Juxiting(df,
horizon=horizon,
input_size=input_size,
train_steps=train_steps,
val_check_steps=val_check_steps,
early_stop_patience_steps=early_stop_patience_steps,
is_debug=is_debug,
dataset=dataset,
is_train=is_train,
is_fivemodels=is_fivemodels,
val_size=val_size,
test_size=test_size,
settings=settings,
now=now,
etadata=etadata,
modelsindex=modelsindex,
data=data,
is_eta=is_eta,
end_time=end_time,
)
# ex_Model_Juxiting(df,
# horizon=horizon,
# input_size=input_size,
# train_steps=train_steps,
# val_check_steps=val_check_steps,
# early_stop_patience_steps=early_stop_patience_steps,
# is_debug=is_debug,
# dataset=dataset,
# is_train=is_train,
# is_fivemodels=is_fivemodels,
# val_size=val_size,
# test_size=test_size,
# settings=settings,
# now=now,
# etadata=etadata,
# modelsindex=modelsindex,
# data=data,
# is_eta=is_eta,
# end_time=end_time,
# )
logger.info('模型训练完成')

301
main_juxiting_zhoudu.py Normal file
View File

@ -0,0 +1,301 @@
# 读取配置
from lib.dataread import *
from lib.tools import SendMail,exception_logger
from models.nerulforcastmodels import ex_Model_Juxiting,model_losss,model_losss_juxiting,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting
import glob
import torch
torch.set_float32_matmul_precision("high")
def predict_main():
"""
主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。
参数:
signature (BinanceAPI): Binance API 实例。
etadata (EtaReader): ETA 数据读取器实例。
is_eta (bool): 是否从 ETA 获取数据。
data_set (str): 数据集名称。
dataset (str): 数据集路径。
add_kdj (bool): 是否添加 KDJ 指标。
is_timefurture (bool): 是否添加时间衍生特征。
end_time (str): 结束时间。
is_edbnamelist (bool): 是否使用 EDB 名称列表。
edbnamelist (list): EDB 名称列表。
y (str): 预测目标列名。
sqlitedb (SQLiteDB): SQLite 数据库实例。
is_corr (bool): 是否进行相关性分析。
horizon (int): 预测时域。
input_size (int): 输入数据大小。
train_steps (int): 训练步数。
val_check_steps (int): 验证检查步数。
early_stop_patience_steps (int): 早停耐心步数。
is_debug (bool): 是否调试模式。
dataset (str): 数据集名称。
is_train (bool): 是否训练模型。
is_fivemodels (bool): 是否使用五个模型。
val_size (float): 验证集大小。
test_size (float): 测试集大小。
settings (dict): 模型设置。
now (str): 当前时间。
etadata (EtaReader): ETA 数据读取器实例。
modelsindex (list): 模型索引列表。
data (str): 数据类型。
is_eta (bool): 是否从 ETA 获取数据。
返回:
None
"""
global end_time
signature = BinanceAPI(APPID, SECRET)
etadata = EtaReader(signature=signature,
classifylisturl=classifylisturl,
classifyidlisturl=classifyidlisturl,
edbcodedataurl=edbcodedataurl,
edbcodelist=edbcodelist,
edbdatapushurl=edbdatapushurl,
edbdeleteurl=edbdeleteurl,
edbbusinessurl=edbbusinessurl
)
# 获取数据
if is_eta:
logger.info('从eta获取数据...')
signature = BinanceAPI(APPID, SECRET)
etadata = EtaReader(signature=signature,
classifylisturl=classifylisturl,
classifyidlisturl=classifyidlisturl,
edbcodedataurl=edbcodedataurl,
edbcodelist=edbcodelist,
edbdatapushurl=edbdatapushurl,
edbdeleteurl=edbdeleteurl,
edbbusinessurl=edbbusinessurl,
)
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data(data_set=data_set, dataset=dataset) # 原始数据，未处理
if is_market:
logger.info('从市场信息平台获取数据...')
try:
# 如果是测试环境，最高价最低价取excel文档
if server_host == '192.168.100.53':
logger.info('从excel文档获取最高价最低价')
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
else:
logger.info('从市场信息平台获取数据')
df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
except :
logger.info('最高最低价拼接失败')
# 保存到xlsx文件的sheet表
with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
# 数据处理
df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
end_time=end_time)
else:
# 读取数据
logger.info('读取本地数据：' + os.path.join(dataset, data_set))
df,df_zhibiaoliebiao = getdata_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
# 更改预测列名称
df.rename(columns={y: 'y'}, inplace=True)
if is_edbnamelist:
df = df[edbnamelist]
df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
# 保存最新日期的y值到数据库
# 取第一行数据存储到数据库中
first_row = df[['ds', 'y']].tail(1)
# 判断y的类型是否为float
if not isinstance(first_row['y'].values[0], float):
logger.info(f'{end_time}预测目标数据为空，跳过')
return None
# 将最新真实值保存到数据库
if not sqlitedb.check_table_exists('trueandpredict'):
first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
else:
for row in first_row.itertuples(index=False):
row_dict = row._asdict()
row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
check_query = sqlitedb.select_data('trueandpredict', where_condition=f"ds = '{row.ds}'")
if len(check_query) > 0:
set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
sqlitedb.update_data('trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
continue
sqlitedb.insert_data('trueandpredict', tuple(row_dict.values()), columns=row_dict.keys())
# 更新accuracy表的y值
if not sqlitedb.check_table_exists('accuracy'):
pass
else:
update_y = sqlitedb.select_data('accuracy',where_condition="y is null")
if len(update_y) > 0:
logger.info('更新accuracy表的y值')
# 找到update_y 中ds且df中的y的行
update_y = update_y[update_y['ds']<=end_time]
logger.info(f'要更新y的信息：{update_y}')
# try:
for row in update_y.itertuples(index=False):
try:
row_dict = row._asdict()
yy = df[df['ds']==row_dict['ds']]['y'].values[0]
LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0]
HIGH = df[df['ds']==row_dict['ds']]['Brentzgj'].values[0]
sqlitedb.update_data('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
except:
logger.info(f'更新accuracy表的y值失败：{row_dict}')
# except Exception as e:
# logger.info(f'更新accuracy表的y值失败：{e}')
import datetime
# 判断当前日期是不是周一
is_weekday = datetime.datetime.now().weekday() == 0
if is_weekday:
logger.info('今天是周一，更新预测模型')
# 计算最近60天预测残差最低的模型名称
model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
# 删除空值率为90%以上的列
if len(model_results) > 10:
model_results = model_results.dropna(thresh=len(model_results)*0.1,axis=1)
# 删除空行
model_results = model_results.dropna()
modelnames = model_results.columns.to_list()[2:-1]
for col in model_results[modelnames].select_dtypes(include=['object']).columns:
model_results[col] = model_results[col].astype(np.float32)
# 计算每个预测值与真实值之间的偏差率
for model in modelnames:
model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
# 获取每行对应的最小偏差率值
min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
# 获取每行对应的最小偏差率值对应的列名
min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
# 将列名索引转换为列名
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
# 取出现次数最多的模型名称
most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
# 保存结果到数据库
if not sqlitedb.check_table_exists('most_model'):
sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
try:
if is_weekday:
# if True:
logger.info('今天是周一，发送特征预警')
# 上传预警信息到数据库
warning_data_df = df_zhibiaoliebiao.copy()
warning_data_df = warning_data_df[warning_data_df['停更周期']> 3 ][['指标名称', '指标id', '频度','更新周期','指标来源','最后更新时间','停更周期']]
# 重命名列名
warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
from sqlalchemy import create_engine
import urllib
global password
if '@' in password:
password = urllib.parse.quote_plus(password)
engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
warning_data_df['TENANT_CODE'] = 'T0004'
# 插入数据之前查询表数据然后新增id列
existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
if not existing_data.empty:
max_id = existing_data['ID'].astype(int).max()
warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
else:
warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
warning_data_df.to_sql(table_name, con=engine, if_exists='append', index=False)
if is_update_warning_data:
upload_warning_info(len(warning_data_df))
except:
logger.info('上传预警信息到数据库失败')
if is_corr:
df = corr_feature(df=df)
df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
logger.info(f"开始训练模型...")
row, col = df.shape
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
ex_Model_Juxiting(df,
horizon=horizon,
input_size=input_size,
train_steps=train_steps,
val_check_steps=val_check_steps,
early_stop_patience_steps=early_stop_patience_steps,
is_debug=is_debug,
dataset=dataset,
is_train=is_train,
is_fivemodels=is_fivemodels,
val_size=val_size,
test_size=test_size,
settings=settings,
now=now,
etadata=etadata,
modelsindex=modelsindex,
data=data,
is_eta=is_eta,
end_time=end_time,
)
logger.info('模型训练完成')
logger.info('训练数据绘图ing')
model_results3 = model_losss_juxiting(sqlitedb)
logger.info('训练数据绘图end')
# 模型报告
logger.info('制作报告ing')
title = f'{settings}--{end_time}-预测报告' # 报告标题
reportname = f'PP大模型预测报告--{end_time}.pdf' # 报告文件名
reportname = reportname.replace(':', '-') # 替换冒号
pp_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
reportname=reportname,sqlitedb=sqlitedb),
logger.info('制作报告end')
logger.info('模型训练完成')
# # LSTM 单变量模型
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
# # lstm 多变量模型
# ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
# # GRU 模型
# # ex_GRU(df)
# 发送邮件
m = SendMail(
username=username,
passwd=passwd,
recv=recv,
title=title,
content=content,
file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
ssl=ssl,
)
# m.send_mail()
if __name__ == '__main__':
# global end_time
# is_on = True
# # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
# for i_time in pd.date_range('2025-1-20', '2025-2-6', freq='B'):
# end_time = i_time.strftime('%Y-%m-%d')
# try:
# predict_main()
# except:
# pass
predict_main()

48
main_yuanyou.py
View File

@ -186,6 +186,8 @@ def predict_main():
if not sqlitedb.check_table_exists('most_model'):
sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
if is_update_warning_data:
upload_warning_info(len(warning_data_df))
try:
if is_weekday:
@ -229,32 +231,32 @@ def predict_main():
row, col = df.shape
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
# ex_Model(df,
# horizon=horizon,
# input_size=input_size,
# train_steps=train_steps,
# val_check_steps=val_check_steps,
# early_stop_patience_steps=early_stop_patience_steps,
# is_debug=is_debug,
# dataset=dataset,
# is_train=is_train,
# is_fivemodels=is_fivemodels,
# val_size=val_size,
# test_size=test_size,
# settings=settings,
# now=now,
# etadata=etadata,
# modelsindex=modelsindex,
# data=data,
# is_eta=is_eta,
# end_time=end_time,
# )
ex_Model(df,
horizon=horizon,
input_size=input_size,
train_steps=train_steps,
val_check_steps=val_check_steps,
early_stop_patience_steps=early_stop_patience_steps,
is_debug=is_debug,
dataset=dataset,
is_train=is_train,
is_fivemodels=is_fivemodels,
val_size=val_size,
test_size=test_size,
settings=settings,
now=now,
etadata=etadata,
modelsindex=modelsindex,
data=data,
is_eta=is_eta,
end_time=end_time,
)
logger.info('模型训练完成')
logger.info('训练数据绘图ing')
# model_results3 = model_losss(sqlitedb,end_time=end_time)
model_results3 = model_losss(sqlitedb,end_time=end_time)
logger.info('训练数据绘图end')
# 模型报告
@ -262,8 +264,8 @@ def predict_main():
title = f'{settings}--{end_time}-预测报告' # 报告标题
reportname = f'Brent原油大模型预测--{end_time}.pdf' # 报告文件名
reportname = reportname.replace(':', '-') # 替换冒号
# brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
# reportname=reportname,sqlitedb=sqlitedb),
brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
reportname=reportname,sqlitedb=sqlitedb),
logger.info('制作报告end')
logger.info('模型训练完成')

9
models/nerulforcastmodels.py
View File

@ -379,15 +379,16 @@ def ex_Model_Juxiting(df,horizon,input_size,train_steps,val_check_steps,early_st
logger.info('读取模型：'+ filename)
nf = load(filename)
# 测试集预测
# nf_test_preds = nf.cross_validation(df=df_test, val_size=val_size, test_size=test_size, n_windows=None)
# # 测试集预测结果保存
# nf_test_preds.to_csv(os.path.join(dataset,"cross_validation.csv"),index=False)
nf_test_preds = nf.cross_validation(df=df_test, val_size=val_size, test_size=test_size, n_windows=None)
# 测试集预测结果保存
nf_test_preds.to_csv(os.path.join(dataset,"cross_validation.csv"),index=False)
# df_test['ds'] = pd.to_datetime(df_test['ds'], errors='coerce')
df_test['ds'] = pd.to_datetime(df_test['ds'], errors='coerce')
#进行未来时间预测
df_predict=nf.predict(df_test).reset_index()
# 去掉index列
if 'index' in df_predict.columns:
df_predict.drop(columns=['index'], inplace=True)
# 处理非有限值（NA 或 inf），将其替换为 0
df_predict = df_predict.fillna(0)