{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import requests\n", "import json\n", "\n", "from datetime import datetime,timedelta\n", "\n", "# 变量定义\n", "login_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n", "search_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos\"\n", "queryDataListItemNos_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos\"\n", "\n", "\n", "login_push_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n", "upload_url = \"http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList\"\n", "\n", "login_data = {\n", " \"data\": {\n", " \"account\": \"api_dev\",\n", " \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n", " \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n", " \"terminal\": \"API\"\n", " },\n", " \"funcModule\": \"API\",\n", " \"funcOperation\": \"获取token\"\n", "}\n", "\n", "login_push_data = {\n", " \"data\": {\n", " \"account\": \"api_dev\",\n", " \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n", " \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n", " \"terminal\": \"API\"\n", " },\n", " \"funcModule\": \"API\",\n", " \"funcOperation\": \"获取token\"\n", "}\n", "\n", "read_file_path_name = \"液化气数据.xlsx\"\n", "one_cols = []\n", "two_cols = []\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sn\n", "import random\n", "import time\n", "\n", "from plotly import __version__\n", "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n", "\n", "from sklearn import preprocessing\n", "\n", "from pandas import Series,DataFrame\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "import sklearn.datasets as datasets\n", "\n", "#导入机器学习算法模型\n", "from sklearn.linear_model import Lasso\n", "from xgboost import XGBRegressor\n", "\n", "import statsmodels.api as sm\n", "try:\n", " from keras.preprocessing.sequence import TimeseriesGenerator\n", "except:\n", " from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n", "\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "\n", "import xgboost as xgb\n", "from xgboost import plot_importance, plot_tree\n", "from sklearn.metrics import mean_absolute_error\n", "from statsmodels.tools.eval_measures import mse,rmse\n", "from sklearn.model_selection import GridSearchCV\n", "from xgboost import XGBRegressor\n", "import warnings\n", "import pickle\n", "\n", "from sklearn.metrics import mean_squared_error\n", "\n", "#切割训练数据和样本数据\n", "from sklearn.model_selection import train_test_split\n", "\n", "#用于模型评分\n", "from sklearn.metrics import r2_score\n", "\n", "le = preprocessing.LabelEncoder()\n", "\n", "# print(__version__) # requires version >= 1.9.0\n", "\n", "\n", "import cufflinks as cf\n", "cf.go_offline()\n", "\n", "random.seed(100)\n", "\n", "%matplotlib inline\n", "\n", "# 数据获取\n", "\n", "def get_head_auth():\n", " login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))\n", " text = json.loads(login_res.text)\n", " if text[\"status\"]:\n", " token = text[\"data\"][\"accessToken\"]\n", " print('获取的token:',token)\n", " return token\n", " else:\n", " print(\"获取认证失败\")\n", " return None\n", "\n", "\n", "def get_data_value(token, dataItemNoList,date):\n", " search_data = {\n", " \"data\": {\n", " \"date\": date,\n", " \"dataItemNoList\": dataItemNoList\n", " },\n", " \"funcModule\": \"数据项\",\n", " \"funcOperation\": \"查询\"\n", " }\n", " \n", " \n", " headers = {\"Authorization\": token}\n", " search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n", " print('数据项查询参数search_data：')\n", " print(search_data)\n", " print('数据项查询结果search_res：')\n", " print(search_res.text)\n", " \n", " try:\n", " search_value = json.loads(search_res.text)[\"data\"]\n", " \n", " print(\"数据项查询结果:\", search_value)\n", " except json.JSONDecodeError as e:\n", " print(f\"Error decoding JSON: {e}\")\n", " print(\"Response content:\", search_res.text)\n", " return None \n", " if search_value:\n", " return search_value\n", " else:\n", " print(\"今天没有新数据\")\n", " return search_value\n", "\n", "\n", "def get_head_push_auth():\n", " login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))\n", " text = json.loads(login_res.text)\n", " if text[\"status\"]:\n", " token = text[\"data\"][\"accessToken\"]\n", " return token\n", " else:\n", " print(\"获取认证失败\")\n", " return None\n", "\n", "\n", "\n", "def upload_data_to_system(token_push,date):\n", " data = {\n", " \"funcModule\": \"数据表信息列表\",\n", " \"funcOperation\": \"新增\",\n", " \"data\": [\n", " {\"dataItemNo\": \"250855713|Forecast_Price|ACN\",\n", " \"dataDate\": date,\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", " 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", " # df_test = pd.read_csv('定价模型数据收集0212.csv')\n", " df_test = pd.read_excel('液化气数据.xlsx')\n", " df_test.drop([0],inplace=True)\n", " try:\n", " df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)\n", " except:\n", " df_test['Date']=pd.to_datetime(df_test['Date'], format=r'%Y-%m-%d',infer_datetime_format=True)\n", "\n", "\n", " df_test_1 = df_test\n", " df_test_1=df_test_1.fillna(df_test.ffill())\n", " df_test_1=df_test_1.fillna(df_test_1.bfill())\n", "\n", " # 选择用于模型训练的列名称\n", " col_for_training = df_test_1.columns\n", "\n", "\n", "\n", "\n", " import joblib\n", " Best_model_DalyLGPrice = joblib.load(\"日度价格预测_液化气最佳模型.pkl\")\n", " # 最新的一天为最后一行的数据\n", " \n", " df_test_1_Day = df_test_1.tail(1)\n", " # 移除不需要的列\n", " df_test_1_Day.index = df_test_1_Day[\"Date\"]\n", " df_test_1_Day = df_test_1_Day.drop([\"Date\"], axis= 1)\n", " df_test_1_Day=df_test_1_Day.drop('Price',axis=1)\n", " df_test_1_Day=df_test_1_Day.dropna()\n", "\n", " for col in df_test_1_Day.columns:\n", " df_test_1_Day[col] = pd.to_numeric(df_test_1_Day[col],errors='coerce')\n", " #预测今日价格，显示至小数点后两位\n", " Ypredict_Today=Best_model_DalyLGPrice.predict(df_test_1_Day)\n", "\n", " df_test_1_Day['日度预测价格']=Ypredict_Today\n", " print(df_test_1_Day['日度预测价格'])\n", " a = df_test_1_Day['日度预测价格']\n", " a = a[0]\n", " a = float(a)\n", " a = round(a,2)\n", " price_list.append(a)\n", " return a\n", "def optimize_Model():\n", " from sklearn.model_selection import train_test_split\n", " from sklearn.impute import SimpleImputer\n", " from sklearn.preprocessing import OrdinalEncoder\n", " from sklearn.feature_selection import SelectFromModel\n", " from sklearn.metrics import mean_squared_error, r2_score\n", " import pandas as pd\n", "\n", " pd.set_option('display.max_rows',40) \n", " pd.set_option('display.max_columns',40) \n", " df_test = pd.read_excel('液化气数据.xlsx')\n", " df_test.drop([0],inplace=True)\n", " try:\n", " df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)\n", " except:\n", " df_test['Date']=pd.to_datetime(df_test['Date'], format=r'%Y-%m-%d',infer_datetime_format=True)\n", "\n", " \n", " #将缺失值补为前一个或者后一个数值\n", " df_test_1 = df_test\n", " df_test_1=df_test_1.fillna(df_test.ffill())\n", " df_test_1=df_test_1.fillna(df_test_1.bfill())\n", " df_test_1[\"Date\"] = pd.to_datetime(df_test_1[\"Date\"])\n", " df_test_1.index = df_test_1[\"Date\"]\n", " df_test_1 = df_test_1.drop([\"Date\"], axis= 1)\n", " df_test_1 = df_test_1.astype('float')\n", " \n", " \n", " import numpy as np\n", " import pandas as pd\n", " from pandas import Series,DataFrame\n", "\n", " import matplotlib.pyplot as plt\n", "\n", " import sklearn.datasets as datasets\n", "\n", " #导入机器学习算法模型\n", " from sklearn.linear_model import Lasso\n", " from xgboost import XGBRegressor\n", "\n", " import statsmodels.api as sm\n", " try:\n", " from keras.preprocessing.sequence import TimeseriesGenerator\n", " except:\n", " from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n", "\n", " import plotly.express as px\n", " import plotly.graph_objects as go\n", "\n", " import xgboost as xgb\n", " from xgboost import plot_importance, plot_tree\n", " from sklearn.metrics import mean_absolute_error\n", " from statsmodels.tools.eval_measures import mse,rmse\n", " from sklearn.model_selection import GridSearchCV\n", " from xgboost import XGBRegressor\n", " import warnings\n", " import pickle\n", "\n", " from sklearn.metrics import mean_squared_error\n", "\n", " #切割训练数据和样本数据\n", " from sklearn.model_selection import train_test_split\n", "\n", " #用于模型评分\n", " from sklearn.metrics import r2_score\n", "\n", " dataset1=df_test_1.drop('Price',axis=1)#.astype(float)\n", "\n", " y=df_test_1['Price']\n", "\n", " x=dataset1 \n", "\n", " train = x\n", " target = y\n", "\n", " #切割数据样本集合测试集\n", " X_train,x_test,y_train,y_true = train_test_split(train,target,test_size=0.2,random_state=0)\n", "\n", " #模型缩写\n", " Lasso = Lasso(random_state=0)\n", " XGBR = XGBRegressor(random_state=0)\n", " #训练模型\n", " Lasso.fit(X_train,y_train)\n", " XGBR.fit(X_train,y_train)\n", " #模型拟合\n", " y_pre_Lasso = Lasso.predict(x_test)\n", " y_pre_XGBR = XGBR.predict(x_test)\n", "\n", " #计算Lasso、XGBR、RandomForestR、AdaBoostR、GradientBoostingR、BaggingRegressor各模型的R²\n", " Lasso_score = r2_score(y_true,y_pre_Lasso)\n", " XGBR_score=r2_score(y_true,y_pre_XGBR)\n", "\n", " #计算Lasso、XGBR的MSE和RMSE\n", " Lasso_MSE=mean_squared_error(y_true, y_pre_Lasso)\n", " XGBR_MSE=mean_squared_error(y_true, y_pre_XGBR)\n", "\n", " Lasso_RMSE=np.sqrt(Lasso_MSE)\n", " XGBR_RMSE=np.sqrt(XGBR_MSE)\n", " # 将不同模型的不同误差值整合成一个表格\n", " model_results = pd.DataFrame([['Lasso', Lasso_RMSE, Lasso_score],\n", " ['XgBoost', XGBR_RMSE, XGBR_score]],\n", " columns = ['模型(Model)','均方根误差(RMSE)', 'R^2 score'])\n", " #将模型名称(Model)列设置为索引\n", " model_results1=model_results.set_index('模型(Model)')\n", "\n", " model_results1\n", " #定义plot_feature_importance函数，该函数用于计算特征重要性。此部分代码无需调整\n", " def plot_feature_importance(importance,names,model_type):\n", " feature_importance = np.array(importance)\n", " feature_names = np.array(names)\n", "\n", " data={'feature_names':feature_names,'feature_importance':feature_importance}\n", " fi_df = pd.DataFrame(data)\n", "\n", " fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)\n", "\n", " plt.figure(figsize=(10,8))\n", " sn.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])\n", "\n", " plt.title(model_type + \" \"+'FEATURE IMPORTANCE')\n", " plt.xlabel('FEATURE IMPORTANCE')\n", " plt.ylabel('FEATURE NAMES')\n", " from pylab import mpl\n", " %pylab\n", " mpl.rcParams['font.sans-serif'] = ['SimHei']\n", " ## Xgboost 模型参数优化-初步\n", " #参考: https://juejin.im/post/6844903661013827598 \n", " #每次调参时，备选参数数值以同数量级的1、3、10设置即可（比如设置1、3、10，或0.1、0.3、1.0，或0.01,0.03,0.10即可）\n", "\n", " from xgboost import XGBRegressor\n", " from sklearn.model_selection import GridSearchCV\n", "\n", " estimator = XGBRegressor(random_state=0,\n", " nthread=4,\n", " seed=0\n", " )\n", " parameters = {\n", " 'max_depth': range (2, 11, 2), # 树的最大深度\n", " 'n_estimators': range (50, 101, 10), # 迭代次数\n", " 'learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]\n", " }\n", "\n", " grid_search_XGB = GridSearchCV(\n", " estimator=estimator,\n", " param_grid=parameters,\n", " # n_jobs = 10,\n", " cv = 3,\n", " verbose=True\n", " )\n", "\n", " grid_search_XGB.fit(X_train, y_train)\n", " #如果电脑在此步骤报错，可能是因为计算量太大，超过硬件可支持程度，可注释掉“n_jobs=10”一行\n", "\n", " best_parameters = grid_search_XGB.best_estimator_.get_params()\n", " y_pred = grid_search_XGB.predict(x_test)\n", "\n", " op_XGBR_score = r2_score(y_true,y_pred)\n", " op_XGBR_MSE= mean_squared_error(y_true, y_pred)\n", " op_XGBR_RMSE= np.sqrt(op_XGBR_MSE)\n", "\n", " model_results2 = pd.DataFrame([['Optimized_Xgboost', op_XGBR_RMSE, op_XGBR_score]],\n", " columns = ['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])\n", " model_results2=model_results2.set_index('模型(Model)')\n", "\n", " try:\n", " results = model_results1.append(model_results2, ignore_index = False)\n", " except:\n", " results = pd.concat([model_results1,model_results2],ignore_index=True)\n", " import pickle\n", "\n", " Pkl_Filename = \"日度价格预测_液化气最佳模型.pkl\" \n", "\n", " with open(Pkl_Filename, 'wb') as file: \n", " pickle.dump(grid_search_XGB, file)\n", "\n", "def read_xls_data():\n", " \"\"\"获取特征项ID\"\"\"\n", " global one_cols, two_cols\n", " # 使用pandas读取Excel文件\n", " df = pd.read_excel(read_file_path_name, header=None) # 不自动识别列名\n", " # 获取第二行数据（索引为1）\n", " one_cols = df.iloc[1].tolist()[1:]\n", " print(f'获取到的数据项ID{one_cols}')\n", "\n", "\n", "def start(date=''):\n", " \"\"\"获取当日数据\"\"\"\n", " read_xls_data()\n", " token = get_head_auth()\n", " if not token:\n", " return\n", " \n", " cur_time,cur_time2 = getNow(date)\n", " print(f\"获取{cur_time}数据\")\n", " datas = get_data_value(token, one_cols,date=cur_time)\n", " if not datas:\n", " return\n", "\n", " append_rows = [cur_time2]\n", " dataItemNo_dataValue = {}\n", " for data_value in datas:\n", " if \"dataValue\" not in data_value:\n", " print(data_value)\n", " dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n", " else:\n", " dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n", " \n", " for value in one_cols:\n", " if value in dataItemNo_dataValue:\n", " append_rows.append(dataItemNo_dataValue[value])\n", " else:\n", " append_rows.append(\"\")\n", " print('添加的行：',append_rows)\n", " save_xls_2(append_rows)\n", "\n", "\n", "def getNow(date='', offset=0):\n", " \"\"\"生成指定日期的两种格式字符串\n", " Args:\n", " date: 支持多种输入类型：\n", " - datetime对象\n", " - 字符串格式（支持'%Y-%m-%d'和'%Y%m%d'）\n", " - 空字符串表示当前日期\n", " offset: 日期偏移天数\n", " Returns:\n", " tuple: (紧凑日期字符串, 标准日期字符串)\n", " \"\"\"\n", " # 日期解析逻辑\n", " if isinstance(date, datetime):\n", " now = date\n", " else:\n", " now = datetime.now()\n", " if date:\n", " # 尝试多种日期格式解析\n", " for fmt in ('%Y-%m-%d', '%Y%m%d', '%Y/%m/%d'):\n", " try:\n", " now = datetime.strptime(str(date), fmt)\n", " break\n", " except ValueError:\n", " continue\n", " else:\n", " raise ValueError(f\"无法解析的日期格式: {date}\")\n", "\n", " # 应用日期偏移\n", " now = now - timedelta(days=offset)\n", " \n", " # 统一格式化输出\n", " date_str = now.strftime(\"%Y-%m-%d\")\n", " compact_date = date_str.replace(\"-\", \"\")\n", " return compact_date, date_str\n", "\n", "def start_1(date=''):\n", " \"\"\"补充昨日数据\"\"\"\n", " read_xls_data()\n", " token = get_head_auth()\n", " if not token:\n", " return\n", " \n", " cur_time,cur_time2 = getNow(date,offset=1)\n", " print(f\"补充{cur_time}数据\")\n", " datas = get_data_value(token, one_cols,date=cur_time)\n", " if not datas:\n", " print(f\"{cur_time}没有数据\")\n", " return\n", "\n", " append_rows = [cur_time2]\n", " dataItemNo_dataValue = {}\n", " for data_value in datas:\n", " if \"dataValue\" not in data_value:\n", " print(data_value)\n", " dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n", " else:\n", " dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n", " \n", " for value in one_cols:\n", " if value in dataItemNo_dataValue:\n", " append_rows.append(dataItemNo_dataValue[value])\n", " else:\n", " append_rows.append(\"\")\n", " print('添加的行：',append_rows)\n", " save_xls_2(append_rows)\n", "\n", "\n", "def save_xls_2(append_rows):\n", " \"\"\"保存或更新数据到Excel文件\n", " 参数:\n", " append_rows (list): 需要追加/更新的数据行，格式为[日期, 数据项1, 数据项2,...]\n", " \"\"\"\n", " try:\n", " # 读取现有数据（假设第一行为列名）\n", " df = pd.read_excel('液化气数据.xlsx', sheet_name=0)\n", " # 转换append_rows为DataFrame\n", " append_rows = pd.DataFrame([append_rows],columns=df.columns)\n", " # 创建新数据行\n", " new_date = append_rows['Date'].values[0]\n", " \n", " dates = df['Date'].to_list()\n", " # 判断日期是否存在\n", " if new_date in dates:\n", " # 找到日期所在行的索引\n", " date_mask = df['Date'] == new_date\n", " # 存在则更新数据\n", " df.loc[date_mask] = append_rows.values\n", " print(f\"更新 {new_date} 数据\")\n", " else:\n", " # 不存在则追加数据\n", " df = pd.concat([df, append_rows], ignore_index=True)\n", " print(df.head())\n", " print(df.tail())\n", " print(f\"插入 {new_date} 新数据\")\n", " \n", " # 保存更新后的数据\n", " df.to_excel('液化气数据.xlsx', index=False, engine='openpyxl')\n", " \n", " except FileNotFoundError:\n", " # 如果文件不存在则创建新文件\n", " pd.DataFrame([append_rows]).to_excel('液化气数据.xlsx', index=False, engine='openpyxl')\n", " except Exception as e:\n", " print(f\"保存数据时发生错误: {str(e)}\")\n", "\n", "def check_data(dataItemNo):\n", " token = get_head_auth()\n", " if not token:\n", " return\n", "\n", " datas = get_data_value(token, dataItemNo)\n", " if not datas:\n", " return\n", "\n", "def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd):\n", "\n", " search_data = {\n", " \"funcModule\": \"数据项\",\n", " \"funcOperation\": \"查询\",\n", " \"data\": {\n", " \"dateStart\": dateStart,\n", " \"dateEnd\": dateEnd,\n", " \"dataItemNoList\": dataItemNoList # 数据项编码,代表 brent最低价和最高价\n", " }\n", " }\n", "\n", " headers = {\"Authorization\": token}\n", " search_res = requests.post(url=url, headers=headers, json=search_data, timeout=(3, 5))\n", " search_value = json.loads(search_res.text)[\"data\"]\n", " if search_value:\n", " return search_value\n", " else:\n", " return None\n", "\n", "def save_queryDataListItemNos_xls(data_df,dataItemNoList):\n", " current_year_month = datetime.now().strftime('%Y-%m')\n", " grouped = data_df.groupby(\"dataDate\")\n", "\n", " # 使用openpyxl打开xlsx文件\n", " from openpyxl import load_workbook\n", " workbook = load_workbook('液化气数据.xlsx')\n", "\n", " # 创建新工作簿\n", " new_workbook = load_workbook('液化气数据.xlsx')\n", " \n", " for sheetname in workbook.sheetnames:\n", " sheet = workbook[sheetname]\n", " new_sheet = new_workbook[sheetname]\n", " \n", " current_year_month_row = 0\n", " # 查找当前月份数据起始行\n", " for row_idx, row in enumerate(sheet.iter_rows(values_only=True), 1):\n", " if str(row[0]).startswith(current_year_month):\n", " current_year_month_row += 1\n", "\n", " # 追加新数据\n", " if sheetname == workbook.sheetnames[0]:\n", " start_row = sheet.max_row - current_year_month_row + 1\n", " for row_idx, (date, group) in enumerate(grouped, start=start_row):\n", " new_sheet.cell(row=row_idx, column=1, value=date)\n", " for j, dataItemNo in enumerate(dataItemNoList, start=2):\n", " if group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values:\n", " new_sheet.cell(row=row_idx, column=j, \n", " value=group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values[0])\n", "\n", " # 保存修改后的xlsx文件\n", " new_workbook.save(\"液化气数据.xlsx\")\n", "\n", "\n", "def queryDataListItemNos(date=None,token=None):\n", " df = pd.read_excel('液化气数据.xlsx')\n", " dataItemNoList = df.iloc[0].tolist()[1:]\n", " if token is None:\n", " token = get_head_auth()\n", " if not token:\n", " print('token获取失败')\n", " return\n", " # 获取当前日期\n", " if date is None:\n", " current_date = datetime.now()\n", " else:\n", " current_date = date\n", " # 获取当月1日\n", " first_day_of_month = current_date.replace(day=1)\n", " # 格式化为 YYYYMMDD 格式\n", " dateEnd = current_date.strftime('%Y%m%d')\n", " dateStart = first_day_of_month.strftime('%Y%m%d')\n", " search_value = get_queryDataListItemNos_value(token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd)\n", " data_df = pd.DataFrame(search_value)\n", " data_df[\"dataDate\"] = pd.to_datetime(data_df[\"dataDate\"])\n", " data_df[\"dataDate\"] = data_df[\"dataDate\"].dt.strftime('%Y-%m-%d')\n", " save_queryDataListItemNos_xls(data_df,dataItemNoList)\n", " print('当月数据更新完成')\n", "\n", "\n", "\n", "def main(start_date=None,token=None,token_push=None):\n", " if start_date is None:\n", " start_date = datetime.now()\n", " if token is None:\n", " token = get_head_auth()\n", " if token_push is None:\n", " token_push = get_head_push_auth()\n", " date = start_date.strftime('%Y%m%d')\n", " print(date)\n", " # 更新当月数据\n", " queryDataListItemNos(start_date,token)\n", " # 更新当日数据\n", " # start(date)\n", " # 训练模型\n", " optimize_Model()\n", " # 预测&上传预测结果\n", " upload_data_to_system(token_push,start_date)\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [], "source": [ "\n", "# if __name__ == \"__main__\":\n", "# print('运行中')\n", "# # 需要单独运行放开\n", "# # start()\n", "# # start_1(date='2025-01-22')\n", "# # start_1()\n", "\n", "# # 每天定时12点运行\n", "# while True:\n", "# try:\n", "# # 获取当前时间\n", "# current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n", "# current_time_1 = time.strftime(\"%H:%M:%S\", time.localtime())\n", "# # print(current_time_1)\n", "\n", "\n", "\n", "\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", " # start()\n", "\n", "# # 休眠1秒钟，避免过多占用CPU资源\n", "# time.sleep(1)\n", "\n", "# elif current_time_1 == \"20:00:00\":\n", "# print(\"更新数据\")\n", "# start_1()\n", "# time.sleep(1)\n", "# except:\n", "# print('执行错误')\n", "# time.sleep(1)\n", "\n", "\n", "# # 检测数据准确性, 需要检测放开\n", "# # check_data(\"100028098|LISTING_PRICE\")\n", "# # check_data(\"9137070016544622XB|DAY_Yield\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "运行中ing...\n", "获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0NDE5ODg0NywianRpIjoiZmJlMmI4MzA5NzFmNDBhMzhiZTA5YTZjMDEyZjU4YmQifQ.rGLp0UBfeu5JmoYXbGSgCpkrO2QnlAx8hFbbbDDXC8I\n", "20250409\n", " dataDate dataItemNo dataValue\n", "0 2025-04-01 100028046|LISTING_PRICE 8208.0\n", "1 2025-04-02 100028046|LISTING_PRICE 8244.0\n", "2 2025-04-03 100028046|LISTING_PRICE 8244.0\n", "3 2025-04-04 100028046|LISTING_PRICE 8165.0\n", "4 2025-04-05 100028046|LISTING_PRICE 8114.0\n", ".. ... ... ...\n", "183 2025-04-07 YHQMXBB|C01100008|STRIKE_PRICE 5180.0\n", "184 2025-04-02 YHQMXBB|C01100008|STRIKE_PRICE 5310.0\n", "185 2025-04-01 YHQMXBB|C01100008|STRIKE_PRICE 5260.0\n", "186 2025-04-04 YHQMXBB|C01100008|STRIKE_PRICE 5230.0\n", "187 2025-04-05 YHQMXBB|C01100008|STRIKE_PRICE 5180.0\n", "\n", "[188 rows x 3 columns]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_9964\\3261286938.py:614: 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" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_9964\\3261286938.py:255: UserWarning:\n", "\n", "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", "\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_9964\\3261286938.py:257: 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" ] }, { "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: ['plot', 'random', '__version__', 'datetime']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Date\n", "2025-04-09 5179.792969\n", "Name: 日度预测价格, dtype: float32\n", "预测值： 5179.79\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_9964\\3261286938.py:203: 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_9964\\3261286938.py:205: 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_9964\\3261286938.py:237: 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" ] } ], "source": [ "\n", "if __name__ == \"__main__\":\n", " print(\"运行中ing...\")\n", " # 每天定时12点运行\n", " # while True:\n", " # # 获取当前时间\n", " # current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n", " # try:\n", " # # 判断当前时间是否为执行任务的时间点\n", " # if current_time == \"12:00:00\":\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", " # check_data(\"9137070016544622XB|DAY_Yield\")\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# start_date = datetime(2025, 4, 2)\n", "# end_date = datetime(2025, 4, 3)\n", "# token = get_head_auth()\n", "\n", "# while start_date < end_date:\n", "# date = start_date.strftime('%Y%m%d')\n", "# date2 = start_date.strftime('%Y-%m-%d')\n", "# queryDataListItemNos(date=start_date,token=token)\n", "# updateYesterdayExcelData(date=date2,token=token)\n", "# start(date)\n", "# # # time.sleep(1)\n", "# # start_1(start_date)\n", "# start_date += timedelta(days=1)\n", "# time.sleep(5)\n", "\n", "# # print(price_list)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 4 }