{ "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", "import xlrd\n", "import xlwt\n", "from datetime import datetime\n", "import time\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", "\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 = \"沥青数据项.xls\"\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", "\n", "\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 datetime\n", "import statsmodels.api as sm\n", "# from keras.preprocessing.sequence import TimeseriesGenerator\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", " 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", "\n", " search_data = {\n", " \"data\": {\n", " \"date\": get_cur_time(date)[0],\n", " \"dataItemNoList\": dataItemNoList\n", " },\n", " \"funcModule\": \"数据项\",\n", " \"funcOperation\": \"查询\"\n", " }\n", " headers = {\"Authorization\": token}\n", " search_res = requests.post(url=search_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", " print(\"今天没有新数据\")\n", " return None\n", "\n", "\n", "# xls文件处理\n", "\n", "\n", "\n", "\n", "\n", "def write_xls(data,date):\n", " # 创建一个Workbook对象\n", " workbook = xlwt.Workbook()\n", "\n", " # 创建一个Sheet对象，可指定名称\n", " sheet = workbook.load('Sheet1')\n", "\n", " # 写入数据行\n", " for row_index, row_data in enumerate(data):\n", " for col_index, cell_data in enumerate(row_data):\n", " sheet.write(row_index, col_index, cell_data)\n", "\n", " # 保存Workbook到文件\n", " workbook.save(get_cur_time(date)[0] + '.xls')\n", "\n", "\n", "def get_cur_time(date = ''):\n", " if date == '':\n", " import datetime\n", " now = datetime.datetime.now()\n", " else:\n", " now = date\n", " year = now.year\n", " month = now.month\n", " day = now.day\n", "\n", " if month < 10:\n", " month = \"0\" + str(month)\n", " if day < 10:\n", " day = \"0\" + str(day)\n", " cur_time = str(year) + str(month) + str(day)\n", " cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n", "# cur_time = '20231007'\n", "# cur_time2 = '2023-10-07'\n", " return cur_time, cur_time2\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\": \"C01100036|Forecast_Price|ACN\",\n", " \"dataDate\": get_cur_time(date)[0],\n", " \"dataStatus\": \"add\",\n", " \"dataValue\": forecast_price()\n", " }\n", "\n", " ]\n", " }\n", " headers = {\"Authorization\": token_push}\n", " res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n", " print(res.text)\n", "\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", "def forecast_price():\n", " df_test = pd.read_excel('沥青数据项.xls',sheet_name='数据项历史数据')\n", " df_test.drop([0],inplace=True)\n", " df_test[['汽油执行价','柴油执行价','齐鲁石化销量','齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',\n", " '科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',\n", " '布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',\n", " '隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',\n", " '京博签单量','京博库存量','京博产量','加权平均成交价']] = df_test[['汽油执行价','柴油执行价','齐鲁石化销量',\n", " '齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',\n", " '科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',\n", " '布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',\n", " '隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',\n", " '京博签单量','京博库存量','京博产量','加权平均成交价']].astype('float')\n", " # df_test['日期']=pd.to_datetime(df_test['日期'], format='%d/%m/%Y',infer_datetime_format=True)\n", " df_test['日期']=pd.to_datetime(df_test['日期'], format='%Y-%m-%d',infer_datetime_format=True)\n", "\n", " #查看每个特征缺失值数量\n", " MisVal_Check=df_test.isnull().sum().sort_values(ascending=False)\n", " #去掉缺失值百分比>0.4的特征，去掉这些特征后的新表格命名为df_test_1\n", " df_MisVal_Check = pd.DataFrame(MisVal_Check,)#\n", " df_MisVal_Check_1=df_MisVal_Check.reset_index()\n", " df_MisVal_Check_1.columns=['Variable_Name','Missing_Number'] \n", " df_MisVal_Check_1['Missing_Number']=df_MisVal_Check_1['Missing_Number']/len(df_test)\n", " df_test_1=df_test.drop(df_MisVal_Check_1[df_MisVal_Check_1['Missing_Number']>0.4].Variable_Name,axis = 1)\n", "\n", " #将缺失值补为前一个或者后一个数值\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", " import joblib\n", " Best_model_DalyLGPrice = joblib.load(\"日度价格预测_最佳模型.pkl\")\n", " # 最新的一天为最后一行的数据\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[\"日期\"]\n", " df_test_1_Day = df_test_1_Day.drop([\"日期\"], axis= 1)\n", " df_test_1_Day=df_test_1_Day.drop('京博指导价',axis=1)\n", " df_test_1_Day=df_test_1_Day.dropna()\n", "\n", " # df_test_1_Day\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", " 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", "\n", " pd.set_option('display.max_rows',40) \n", " pd.set_option('display.max_columns',40) \n", " df_test = pd.read_excel('沥青数据项.xls',sheet_name='数据项历史数据')\n", " df_test.drop([0],inplace=True)\n", " df_test[['汽油执行价','柴油执行价','齐鲁石化销量','齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',\n", " '科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',\n", " '布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',\n", " '隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',\n", " '京博签单量','京博库存量','京博产量','加权平均成交价']] = df_test[['汽油执行价','柴油执行价','齐鲁石化销量','齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',\n", " '科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',\n", " '布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',\n", " '隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',\n", " '京博签单量','京博库存量','京博产量','加权平均成交价']].astype('float')\n", " # df_test = pd.read_csv('定价模型数据收集20190901-20230615.csv',encoding = 'gbk',engine = 'python')\n", " # df_test['日期']=pd.to_datetime(df_test['日期'], format='%m/%d/%Y',infer_datetime_format=True)\n", " df_test['日期']=pd.to_datetime(df_test['日期'], format='%Y-%m-%d',infer_datetime_format=True)\n", " # df_test.tail(3)\n", " MisVal_Check=df_test.isnull().sum().sort_values(ascending=False)\n", " #去掉缺失值百分比>0.4的特征，去掉这些特征后的新表格命名为df_test_1\n", " df_MisVal_Check = pd.DataFrame(MisVal_Check,)#\n", " df_MisVal_Check_1=df_MisVal_Check.reset_index()\n", " df_MisVal_Check_1.columns=['Variable_Name','Missing_Number'] \n", " df_MisVal_Check_1['Missing_Number']=df_MisVal_Check_1['Missing_Number']/len(df_test)\n", " df_test_1=df_test.drop(df_MisVal_Check_1[df_MisVal_Check_1['Missing_Number']>0.4].Variable_Name,axis = 1)\n", " #将缺失值补为前一个或者后一个数值\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[\"日期\"] = pd.to_datetime(df_test_1[\"日期\"])\n", " df_test_1.index = df_test_1[\"日期\"]\n", " df_test_1 = df_test_1.drop([\"日期\"], axis= 1)\n", " dataset1=df_test_1.drop('京博指导价',axis=1)#.astype(float)\n", "\n", " y=df_test_1['京博指导价']\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", " \n", " from sklearn.linear_model import Lasso\n", " from xgboost import XGBRegressor\n", "\n", " from datetime import datetime\n", " import statsmodels.api as sm\n", " # from keras.preprocessing.sequence import TimeseriesGenerator\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", " #模型缩写\n", " Lasso = Lasso(random_state=0)\n", " XGBR = XGBRegressor(random_state=0)\n", " Lasso.fit(X_train,y_train)\n", " XGBR.fit(X_train,y_train)\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", " 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", " model_results1=model_results.set_index('模型(Model)')\n", "\n", " model_results1\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", " 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", " print(\"Best score: %0.3f\" % grid_search_XGB.best_score_)\n", " print(\"Best parameters set:\")\n", " best_parameters = grid_search_XGB.best_estimator_.get_params()\n", " for param_name in sorted(parameters.keys()):\n", " print(\"\\t%s: %r\" % (param_name, best_parameters[param_name]))\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", " # results = model_results1.append(model_results2, ignore_index = False)\n", " results = pd.concat([model_results1,model_results2],ignore_index=True)\n", " results\n", " import pickle\n", "\n", " Pkl_Filename = \"日度价格预测_最佳模型.pkl\" \n", "\n", " with open(Pkl_Filename, 'wb') as file: \n", " pickle.dump(grid_search_XGB, file)\n", "\n", "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "def read_xls_data():\n", " global one_cols, two_cols\n", " # 打开 XLS 文件\n", " workbook = xlrd.open_workbook(read_file_path_name)\n", "\n", " # 获取所有表格名称\n", " # sheet_names = workbook.sheet_names()\n", "\n", " # 选择第一个表格\n", " sheet = workbook.sheet_by_index(0)\n", "\n", " # 获取行数和列数\n", " num_rows = sheet.nrows\n", " # num_cols = sheet.ncols\n", "\n", " # 遍历每一行，获取单元格数据\n", " # for i in range(num_rows):\n", " # row_data = sheet.row_values(i)\n", " # one_cols.append(row_data)\n", " # two_cols.append(row_data[1])\n", "\n", " row_data = sheet.row_values(1)\n", " one_cols = row_data\n", "\n", " # 关闭 XLS 文件\n", " # workbook.close()\n", "\n", "\n", "\n", "\n", "def start():\n", " '''预测上传数据'''\n", " read_xls_data()\n", "\n", " token = get_head_auth()\n", " if not token:\n", " return\n", " token_push = get_head_push_auth()\n", " if not token_push:\n", " return\n", "\n", " datas = get_data_value(token, one_cols[1:])\n", " if not datas:\n", " return\n", "\n", " # data_list = [two_cols, one_cols]\n", " append_rows = [get_cur_time()[1]]\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[1:]:\n", " if value in dataItemNo_dataValue:\n", " append_rows.append(dataItemNo_dataValue[value])\n", " else:\n", " append_rows.append(\"\")\n", " save_xls(append_rows)\n", " optimize_Model()\n", " upload_data_to_system(token_push)\n", " # data_list.append(three_cols)\n", " # write_xls(data_list)\n", "\n", "\n", "def start_3(date):\n", " '''预测上传数据'''\n", " read_xls_data()\n", "\n", " token = get_head_auth()\n", " if not token:\n", " return\n", " token_push = get_head_push_auth()\n", " if not token_push:\n", " return\n", "\n", " datas = get_data_value(token, one_cols[1:],date)\n", " if not datas:\n", " return\n", "\n", " # data_list = [two_cols, one_cols]\n", " append_rows = [get_cur_time(date)[1]]\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[1:]:\n", " if value in dataItemNo_dataValue:\n", " append_rows.append(dataItemNo_dataValue[value])\n", " else:\n", " append_rows.append(\"\")\n", " save_xls(append_rows)\n", " optimize_Model()\n", " upload_data_to_system(token_push,date)\n", " # data_list.append(three_cols)\n", " # write_xls(data_list)\n", "\n", "\n", "\n", "def start_1():\n", " '''更新数据'''\n", " read_xls_data()\n", "\n", " token = get_head_auth()\n", " if not token:\n", " return\n", " \n", "\n", " datas = get_data_value(token, one_cols[1:])\n", " if not datas:\n", " return\n", "\n", " # data_list = [two_cols, one_cols]\n", " append_rows = [get_cur_time()[1]]\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[1:]:\n", " if value in dataItemNo_dataValue:\n", " append_rows.append(dataItemNo_dataValue[value])\n", " else:\n", " append_rows.append(\"\")\n", " save_xls_1(append_rows)\n", "\n", " \n", " # data_list.append(three_cols)\n", " # write_xls(data_list)\n", "\n", "\n", "def start_2(date):\n", " '''更新数据'''\n", " read_xls_data()\n", "\n", " token = get_head_auth()\n", " if not token:\n", " return\n", " \n", "\n", " datas = get_data_value(token, one_cols[1:],date)\n", " if not datas:\n", " return\n", "\n", " # data_list = [two_cols, one_cols]\n", " append_rows = [get_cur_time(date=date)[1]]\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[1:]:\n", " if value in dataItemNo_dataValue:\n", " append_rows.append(dataItemNo_dataValue[value])\n", " else:\n", " append_rows.append(\"\")\n", " print('新增数据：',append_rows)\n", " save_xls_1(append_rows)\n", "\n", " \n", " # data_list.append(three_cols)\n", " # write_xls(data_list)\n", " \n", "def save_xls_1(append_rows):\n", "\n", " # 打开xls文件\n", " workbook = xlrd.open_workbook('沥青数据项.xls')\n", "\n", " # 获取所有sheet的个数\n", " sheet_count = len(workbook.sheet_names())\n", "\n", " # 获取所有sheet的名称\n", " sheet_names = workbook.sheet_names()\n", "\n", " new_workbook = xlwt.Workbook()\n", " for i in range(sheet_count):\n", " # 获取当前sheet\n", " sheet = workbook.sheet_by_index(i)\n", "\n", " # 获取sheet的行数和列数\n", " row_count = sheet.nrows - 1\n", " col_count = sheet.ncols\n", " # 获取原有数据\n", " data = []\n", " for row in range(row_count):\n", " row_data = []\n", " for col in range(col_count):\n", " row_data.append(sheet.cell_value(row, col))\n", " data.append(row_data)\n", " # 创建xlwt的Workbook对象\n", " # 创建sheet\n", " new_sheet = new_workbook.add_sheet(sheet_names[i])\n", "\n", " # 将原有的数据写入新的sheet\n", " for row in range(row_count):\n", " for col in range(col_count):\n", " new_sheet.write(row, col, data[row][col])\n", "\n", " if i == 0:\n", " # 在新的sheet中添加数据\n", " for col in range(col_count):\n", " new_sheet.write(row_count, col, append_rows[col])\n", "\n", " # 保存新的xls文件\n", " new_workbook.save(\"沥青数据项.xls\") \n", "\n", " \n", " \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", "\n", "def save_xls(append_rows):\n", "\n", " # 打开xls文件\n", " workbook = xlrd.open_workbook('沥青数据项.xls')\n", "\n", " # 获取所有sheet的个数\n", " sheet_count = len(workbook.sheet_names())\n", "\n", " # 获取所有sheet的名称\n", " sheet_names = workbook.sheet_names()\n", "\n", " new_workbook = xlwt.Workbook()\n", " for i in range(sheet_count):\n", " # 获取当前sheet\n", " sheet = workbook.sheet_by_index(i)\n", "\n", " # 获取sheet的行数和列数\n", " row_count = sheet.nrows\n", " col_count = sheet.ncols\n", " # 获取原有数据\n", " data = []\n", " for row in range(row_count):\n", " row_data = []\n", " for col in range(col_count):\n", " row_data.append(sheet.cell_value(row, col))\n", " data.append(row_data)\n", " # 创建xlwt的Workbook对象\n", " # 创建sheet\n", " new_sheet = new_workbook.add_sheet(sheet_names[i])\n", "\n", " # 将原有的数据写入新的sheet\n", " for row in range(row_count):\n", " for col in range(col_count):\n", " new_sheet.write(row, col, data[row][col])\n", "\n", " if i == 0:\n", " # 在新的sheet中添加数据\n", " for col in range(col_count):\n", " new_sheet.write(row_count, col, append_rows[col])\n", "\n", " # 保存新的xls文件\n", " new_workbook.save(\"沥青数据项.xls\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " pass\n", " # 需要单独运行放开\n", "# start()\n", "\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", " # current_time_1 = time.strftime(\"%H:%M:%S\", time.localtime())\n", "\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", " # 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", "\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": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20241025\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_6896\\1185823687.py:299: 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: \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: ['random', 'datetime', 'plot', '__version__']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best score: 0.997\n", "Best parameters set:\n", "\tlearning_rate: 0.1\n", "\tmax_depth: 10\n", "\tn_estimators: 100\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Hello\\AppData\\Local\\Temp\\ipykernel_6896\\1185823687.py:239: 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_6896\\1185823687.py:273: 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": [ "日期\n", "2024-10-25 3538.790283\n", "Name: 日度预测价格, dtype: float32\n", "{\"confirmFlg\":false,\"status\":true}\n", "新增数据： ['2024-10-25', '', '', 0.2, 0.0, 3660.0, 1.5, 0.1, 0.0, 3450.0, 7.9, 0.2, 0.2, 3450.0, 0.3, '', 3500.0, 75.46, 72.67, '', '', '', '', '', '', '', '', '', 3065.6453, '', '', '', '', '']\n" ] } ], "source": [ "from datetime import datetime, timedelta\n", "\n", "start_date = datetime(2024, 10, 25)\n", "end_date = datetime(2024, 10, 26)\n", "\n", "while start_date < end_date:\n", " print(start_date.strftime('%Y%m%d'))\n", " start_3(start_date)\n", " time.sleep(1)\n", " start_2(start_date)\n", " start_date += timedelta(days=1)" ] }, { "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 }