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PriceForecast/aisenzhecode/沥青/沥青定量价格预测每日推送-ytj.ipynb

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添加埃森哲价格预测代码
2024-11-01 17:33:48 +08:00
{
"cells": [
{
"cell_type": "code",
沥青定量更新
2024-12-02 10:51:24 +08:00
"execution_count": 1,
添加埃森哲价格预测代码
2024-11-01 17:33:48 +08:00
"metadata": {},
"outputs": [
沥青定量更新
2024-12-02 10:51:24 +08:00
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From C:\\Users\\EDY\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
"\n"
]
},
添加埃森哲价格预测代码
2024-11-01 17:33:48 +08:00
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" requirejs.config({\n",
" paths: {\n",
" 'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
" }\n",
" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\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",
沥青定量更新
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"execution_count": 3,
添加埃森哲价格预测代码
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
沥青定量更新
2024-12-02 10:51:24 +08:00
"20241031\n"
添加埃森哲价格预测代码
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]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
沥青定量更新
2024-12-02 10:51:24 +08:00
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.py:299: UserWarning:\n",
添加埃森哲价格预测代码
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"\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": [
沥青定量更新
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"Using matplotlib backend: <object object at 0x000001EAB4F2F0A0>\n",
添加埃森哲价格预测代码
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"%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",
沥青定量更新
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"pylab import has clobbered these variables: ['datetime', 'plot', '__version__', 'random']\n",
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"`%matplotlib` prevents importing * from pylab and numpy\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
沥青定量更新
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"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 6\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-10-31 3497.486084\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-10-31', 7294.0, 6913.0, 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, '', 3277.0, 27.2989, 72.7, 231.1, 247.5, '', 229522.1, 6972.02, 3065.6453, '', '', 72263.104377, 6282.3225, 3619.22]\n",
"20241101\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
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"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": [
沥青定量更新
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"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-01 3464.738525\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-01', 7294.0, 6913.0, 0.2, 0.0, 3660.0, 1.3, 0.1, 0.0, 3450.0, 7.9, 0.2, 0.2, 3450.0, 0.3, '', 3450.0, 75.46, 74.06, '', 3276.0, 28.0378, '', '', '', '', 229522.1, 6351.32, 3244.8191, '', '', 73599.8538493, 8140.4045, 3469.29]\n",
"20241102\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-02 3440.8125\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-02', 7294.0, 6913.0, 0.2, 0.0, 3660.0, 1.1, 0.1, 0.0, 3420.0, 7.9, 0.2, 0.2, 3450.0, 0.3, '', 3430.0, 75.46, 72.94, '', '', 23.8506, '', '', '', '', 229522.1, 7268.02, 3151.1746, '', '', 73516.43677, 7131.0735, 3530.0]\n",
"20241103\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-03 3439.636475\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-03', 7294.0, 6913.0, 0.15, 0.0, 3660.0, 0.95, 0.1, 0.0, 3420.0, 7.9, 0.1, 0.2, 3450.0, 0.4, '', 3430.0, 72.94, '', '', '', 24.2611, '', '', '', '', 229522.1, 8880.62, 3150.6622, '', '', 72306.1990126, 7150.9, 3606.67]\n",
"20241104\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-04 3431.356445\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-04', 7250.0, 6874.0, 0.1, 0.0, 3660.0, 0.85, 0.1, 0.0, 3420.0, 7.9, 0.1, 0.2, 3450.0, 0.5, '', 3430.0, 72.94, '', '', 3260.0, 27.1346, '', '', '', '', 229522.1, 5903.98, 3279.8531, '', '', 72752.256128, 6937.3, 3440.84]\n",
"20241105\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.py:239: UserWarning:\n",
添加埃森哲价格预测代码
2024-11-01 17:33:48 +08:00
"\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",
沥青定量更新
2024-12-02 10:51:24 +08:00
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.py:273: FutureWarning:\n",
添加埃森哲价格预测代码
2024-11-01 17:33:48 +08:00
"\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",
沥青定量更新
2024-12-02 10:51:24 +08:00
"2024-11-05 3449.845947\n",
添加埃森哲价格预测代码
2024-11-01 17:33:48 +08:00
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
沥青定量更新
2024-12-02 10:51:24 +08:00
"新增数据： ['2024-11-05', 7267.0, 6939.0, 0.15, 0.0, 3610.0, 0.7, 0.1, 0.0, 3360.0, 7.9, 0.1, 0.2, 3400.0, 0.6, '', 3450.0, 72.94, 75.2, '', 3301.0, 28.9819, '', '', '', 17.44766773, 229522.1, 9502.9, 3320.7624, '', 720.0, 70363.8172169, 6851.5515, 3458.58]\n",
"20241106\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-06 3454.748535\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-06', 7267.0, 6939.0, 0.15, 0.25, 3610.0, 0.8, 0.1, 0.0, 3380.0, 7.9, 0.2, 0.2, 3400.0, 0.6, '', 3450.0, 72.94, 74.07, '', 3302.0, 31.2397, '', '', '', '', 229522.1, 8324.34, 3334.8235, '', 17650.0, 68981.7674045, 7022.693, 3273.91]\n",
"20241107\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-07 3451.568115\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-07', 7267.0, 6939.0, 0.15, 0.25, 3610.0, 0.9, 0.1, 0.0, 3380.0, 7.9, 0.2, 0.2, 3400.0, 0.6, '', 3470.0, 72.94, 75.16, '', 3365.0, 33.087, '', '', '', '', 229522.1, 8777.919, 3411.0773, '', 2000.0, 66779.8573794, '', 3250.0]\n",
"20241108\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-08 3440.411133\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-08', 7267.0, 6939.0, 0.15, 0.25, 3610.0, 1.0, 0.1, 0.0, 3380.0, 7.9, 0.3, 0.2, 3400.0, 0.5, '', 3470.0, 72.94, 75.57, '', 3345.0, 31.5271, '', '', '', '', 229522.1, 7288.82, 3374.9369, '', 2150.0, 66716.4640638, 7339.8715, 3659.07]\n",
"20241109\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 70\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-09 3460.796143\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-09', 7267.0, 6939.0, 0.15, 0.25, 3610.0, 1.1, 0.1, 0.0, 3380.0, 7.9, 0.3, 0.2, 3400.0, 0.5, '', 3450.0, 72.94, 73.63, '', '', 31.9376, '', '', '', '', 229522.1, 8775.16, 3284.5165, '', 150.0, 65633.9467102, '', 3450.4]\n",
"20241110\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-10 3437.404541\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-10', 7267.0, 6904.0, 0.15, 0.25, 3610.0, 1.2, 0.1, 0.0, 3380.0, 7.9, 0.3, 0.2, 3400.0, 0.5, '', 3450.0, 73.63, '', '', '', 31.9376, '', '', '', '', 229522.1, 7529.48, 3299.2769, '', 370.0, 65923.156478, 7140.168, 3530.16]\n",
"20241111\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 10\n",
"\tn_estimators: 90\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-11 3455.689697\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-11', 7238.0, 6874.0, 0.15, 0.25, 3610.0, 1.3, 0.1, 0.0, 3380.0, 7.9, 0.3, 0.2, 3400.0, 0.5, '', 3430.0, 73.63, '', '', 3364.0, 32.1839, '', '', '', '', 229522.1, 6774.98, 3293.8831, '', 3050.0, 65569.0377442, 7088.87, 3474.75]\n",
"20241112\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 90\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-12 3450.757324\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-12', 7205.0, 6847.0, 0.15, 0.25, 3610.0, 1.4, 0.1, 0.0, 3380.0, 7.9, 0.3, 0.2, 3400.0, 0.4, '', 3430.0, 73.63, 71.9, '', 3317.0, 33.8259, '', '', '', 16.2201973, 229522.1, 6656.48, 3142.6481, '', 1300.0, 66110.4355813, 7467.25, 3673.08]\n",
"20241113\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 80\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-13 3435.947998\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-13', 7205.0, 6847.0, 0.15, 0.25, 3610.0, 1.5, 0.1, 0.0, 3380.0, 7.9, 0.15, 0.2, 3400.0, 0.45, '', 3400.0, 73.63, 71.79, '', 3329.0, 31.8966, '', '', '', '', 229522.1, 8931.91, 3201.4761, '', 564.0, 64630.0816828, 7473.8675, 3551.09]\n",
"20241114\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 6\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-14 3430.023438\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-14', 7205.0, 6847.0, 0.15, 0.25, 3610.0, 1.5, 0.1, 0.0, 3360.0, 7.9, 0.35, 0.2, 3350.0, 0.3, '', 3400.0, 73.63, 72.03, '', 3294.0, 31.0755, '', '', '', '', 229522.1, 8104.28, 3208.5619, '', 3500.0, 64315.0755356, 7315.9015, 3462.86]\n",
"20241115\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-15 3415.211426\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-15', 7283.0, 6865.0, 0.15, 0.25, 3610.0, 1.6, 0.1, 0.0, 3360.0, 7.9, 0.35, 0.2, 3350.0, 0.3, '', 3380.0, 73.63, 72.41, '', 3262.0, 31.0755, '', '', '', '', 229522.1, 8164.44, 3190.3405, '', 2785.0, 62868.9463144, 7032.4715, 3463.29]\n",
"20241116\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-16 3395.489502\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-16', 7283.0, 6865.0, 0.15, 0.25, 3610.0, 1.8, 0.0, 0.0, 3360.0, 7.9, 0.35, 0.2, 3350.0, 0.3, '', 3380.0, 73.63, 71.05, '', '', 32.5534, '', '', '', '', 229522.1, 7950.56, 3088.3104, '', 2450.0, 62578.6476011, 7132.0055, 3399.59]\n",
"20241117\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-17 3385.806152\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-17', 7283.0, 6830.0, 0.15, 0.25, 3610.0, 1.9, 0.0, 0.0, 3360.0, 7.9, 0.2, 0.2, 3350.0, 0.3, '', 3380.0, 71.05, '', '', '', 32.3071, '', '', '', '', 229522.1, 5124.22, 3100.5196, '', 1850.0, 64235.5188737, 7305.2595, 3402.7]\n",
"20241118\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 6\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-18 3385.220947\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-18', 7283.0, 6803.0, 0.15, 0.25, 3610.0, 2.0, 0.0, 0.0, 3360.0, 7.9, 0.2, 0.2, 3350.0, 0.3, '', 3380.0, 71.05, '', '', 3301.0, 32.3071, '', '', '', '', 229522.1, 4642.92, 3126.3124, '', 4000.0, 66756.6918197, 7031.2, 3395.0]\n",
"20241119\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-19 3408.421631\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-19', 7362.0, 6847.0, 0.15, 0.0, 3560.0, 2.0, 0.0, 0.0, 3360.0, 7.9, 0.2, 0.2, 3350.0, 0.3, '', 3380.0, 71.05, 73.14, '', 3340.0, 33.7438, '', '', '', 15.81179465, 229522.1, 9127.64, 3304.263, '', 11885.0, 64761.4451464, 7150.3265, 3380.0]\n",
"20241120\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-20 3379.709717\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-20', 7454.0, 6847.0, 0.15, 0.0, 3560.0, 1.85, 0.0, 0.0, 3360.0, 7.9, 0.2, 0.2, 3350.0, 0.3, '', 3380.0, 71.05, 73.3, '', 3360.0, 34.3596, '', '', '', '', 229522.1, 7469.46, 3285.3988, '', 5700.0, 64432.0675109, '', 3390.71]\n",
"20241121\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-21 3416.765137\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-21', 7454.0, 6847.0, 0.15, 0.0, 3560.0, 1.7, 0.0, 0.0, 3360.0, 7.9, 0.3, 0.2, 3350.0, 1.2, '', 3420.0, 71.05, 73.1, '', 3367.0, 33.9491, '', '', '', '', 229522.1, 5521.08, 3313.7046, '', '', 66175.5419216, 7188.252, 3420.0]\n",
"20241122\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 10\n",
"\tn_estimators: 90\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-22 3456.814209\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-22', 7489.0, 6904.0, 0.15, 0.0, 3560.0, 1.55, 0.0, 0.0, 3380.0, 7.9, 0.3, 0.2, 3400.0, 1.1, '', 3460.0, 71.05, 72.78, '', 3393.0, 34.2365, '', '', '', '', 229522.1, 5216.0, 3387.6293, '', '', 68080.5795859, 7110.9915, 3841.43]\n",
"20241123\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-23 3458.271973\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-23', 7550.0, 6984.0, 0.15, 0.0, 3560.0, 1.4, 0.0, 0.0, 3380.0, 7.9, 0.3, 0.2, 3400.0, 1.0, '', 3460.0, 71.05, 74.73, '', '', 33.8259, '', '', '', '', 229522.1, 6253.74, 3383.6883, '', '', 68880.5006398, 7130.02, 3510.0]\n",
"20241124\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-24 3478.412842\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-24', 7585.0, 7031.0, 0.15, 0.0, 3560.0, 1.25, 0.0, 0.0, 3400.0, 7.9, 0.3, 0.2, 3450.0, 0.9, '', 3480.0, 74.73, '', '', '', 33.5386, '', '', '', '', 229522.1, 5726.1, 3363.654, '', '', 70591.2921694, 7271.94, 3658.25]\n",
"20241125\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-25 3480.252686\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-25', 7585.0, 7031.0, 0.15, 0.0, 3560.0, 0.8, 0.0, 0.0, 3400.0, 7.9, 0.3, 0.2, 3450.0, 0.8, '', 3480.0, 74.73, '', '', 3397.0, 32.9228, '', '', '', '', 229522.1, 6558.76, 3345.8341, '', '', 71325.7122334, 7159.21, 3713.73]\n",
"20241126\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 90\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-26 3472.18042\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-26', 7550.0, 6957.0, 0.1, 0.0, 3560.0, 0.7, 0.0, 0.0, 3380.0, 7.9, 0.2, 0.2, 3450.0, 0.8, '', 3480.0, 74.73, 72.55, '', 3392.0, 32.9228, '', '', '', 15.12113643, 229522.1, 4880.42, 3208.0829, '', '', 73431.7903161, 7051.13, 3480.0]\n",
"20241127\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-27 3478.073975\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-27', 7523.0, 6913.0, 0.15, 0.0, 3560.0, 0.55, 0.0, 0.0, 3380.0, 7.9, 0.2, 0.2, 3410.0, 0.8, '', 3480.0, 74.73, 72.39, '', 3402.0, 32.9228, '', '', '', '', 229522.1, 6551.58, 3207.2044, '', '', 73610.6537987, 7022.57, 3510.0]\n",
"20241128\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 80\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-28 3461.341797\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-28', 7523.0, 6913.0, 0.1, 0.25, 3560.0, 0.55, 0.0, 0.0, 3380.0, 7.9, 0.2, 0.2, 3410.0, 0.8, '', 3450.0, 74.73, 72.38, '', 3437.0, 33.1691, '', '', '', '', 229522.1, 6888.84, 3204.8735, '', '', 74014.7705047, 7340.35, 3627.05]\n",
"20241129\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.3\n",
"\tmax_depth: 8\n",
"\tn_estimators: 50\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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",
"2024-11-29 3452.575684\n",
"Name: 日度预测价格, dtype: float32\n",
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-29', 7550.0, 6957.0, 0.08, 0.0, 3560.0, 0.5, 0.0, 0.0, 3380.0, 7.9, 0.2, 0.2, 3420.0, 0.8, '', 3450.0, 74.73, 72.69, '', 3512.0, 31.5271, '', '', '', '', 229522.1, 4979.12, 3226.8375, '', '', 75783.1991118, 7244.5765, 3466.36]\n",
"20241130\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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: 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",
"Best score: 0.997\n",
"Best parameters set:\n",
"\tlearning_rate: 0.1\n",
"\tmax_depth: 8\n",
"\tn_estimators: 100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"日期\n",
"2024-11-30 3503.998047\n",
"Name: 日度预测价格, dtype: float32\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_3076\\2242856620.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": [
"{\"confirmFlg\":false,\"status\":true}\n",
"新增数据： ['2024-11-30', 7576.0, 6957.0, '', '', '', '', '', '', '', '', '', '', '', '', '', 3530.0, 74.73, 71.45, '', '', '', 61.8, '', '', '', 229522.1, 6820.44, 3166.3388, '', 1.0, 76240.6094746, 6410.4865, 3480.56]\n"
添加埃森哲价格预测代码
2024-11-01 17:33:48 +08:00
]
}
],
"source": [
"from datetime import datetime, timedelta\n",
"\n",
沥青定量更新
2024-12-02 10:51:24 +08:00
"start_date = datetime(2024, 10, 31)\n",
"end_date = datetime(2024, 12, 1)\n",
添加埃森哲价格预测代码
2024-11-01 17:33:48 +08:00
"\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
}
Reference in New Issue Copy Permalink