液化气预测调试
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@ -1,3 +1,30 @@
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from statsmodels.tools.eval_measures import mse, rmse
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from pandas import Series, DataFrame
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import cufflinks as cf
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from sklearn.metrics import r2_score
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error
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import pickle
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import warnings
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from sklearn.model_selection import GridSearchCV
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from sklearn.metrics import mean_absolute_error
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from xgboost import plot_importance, plot_tree
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import xgboost as xgb
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import plotly.graph_objects as go
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import plotly.express as px
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import statsmodels.api as sm
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from xgboost import XGBRegressor
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from sklearn.linear_model import Lasso
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import sklearn.datasets as datasets
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from sklearn import preprocessing
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from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
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from plotly import __version__
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import time
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import random
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import seaborn as sn
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import requests
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import json
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@ -38,60 +65,24 @@ read_file_path_name = "液化气数据.xlsx"
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one_cols = []
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two_cols = []
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sn
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import random
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import time
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from plotly import __version__
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from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
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from sklearn import preprocessing
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from pandas import Series,DataFrame
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import matplotlib.pyplot as plt
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import sklearn.datasets as datasets
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# 导入机器学习算法模型
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from sklearn.linear_model import Lasso
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from xgboost import XGBRegressor
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import statsmodels.api as sm
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try:
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from keras.preprocessing.sequence import TimeseriesGenerator
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except:
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from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
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import plotly.express as px
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import plotly.graph_objects as go
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import xgboost as xgb
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from xgboost import plot_importance, plot_tree
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from sklearn.metrics import mean_absolute_error
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from statsmodels.tools.eval_measures import mse,rmse
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from sklearn.model_selection import GridSearchCV
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from xgboost import XGBRegressor
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import warnings
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import pickle
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from sklearn.metrics import mean_squared_error
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# 切割训练数据和样本数据
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from sklearn.model_selection import train_test_split
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# 用于模型评分
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from sklearn.metrics import r2_score
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le = preprocessing.LabelEncoder()
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# print(__version__) # requires version >= 1.9.0
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import cufflinks as cf
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cf.go_offline()
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random.seed(100)
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@ -121,9 +112,9 @@ def get_data_value(token, dataItemNoList,date):
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"funcOperation": "查询"
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}
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headers = {"Authorization": token}
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search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))
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search_res = requests.post(
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url=search_url, headers=headers, json=search_data, timeout=(3, 5))
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print('数据项查询参数search_data：')
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print(search_data)
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print('数据项查询结果search_res：')
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@ -145,7 +136,8 @@ def get_data_value(token, dataItemNoList,date):
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def get_head_push_auth():
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login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))
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login_res = requests.post(
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url=login_push_url, json=login_push_data, timeout=(3, 5))
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text = json.loads(login_res.text)
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if text["status"]:
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token = text["data"]["accessToken"]
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@ -155,7 +147,6 @@ def get_head_push_auth():
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return None
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def upload_data_to_system(token_push, date):
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data = {
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"funcModule": "数据表信息列表",
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@ -170,22 +161,25 @@ def upload_data_to_system(token_push,date):
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]
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}
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headers = {"Authorization": token_push}
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res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))
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res = requests.post(url=upload_url, headers=headers,
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json=data, timeout=(3, 5))
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print(res.text)
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print('预测值：', data['data'][0]['dataValue'])
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price_list = []
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def forecast_price():
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# df_test = pd.read_csv('定价模型数据收集0212.csv')
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df_test = pd.read_excel('液化气数据.xlsx')
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df_test.drop([0], inplace=True)
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try:
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df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)
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df_test['Date'] = pd.to_datetime(
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df_test['Date'], format='%m/%d/%Y', infer_datetime_format=True)
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except:
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df_test['Date']=pd.to_datetime(df_test['Date'], format=r'%Y-%m-%d',infer_datetime_format=True)
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df_test['Date'] = pd.to_datetime(
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df_test['Date'], format=r'%Y-%m-%d', infer_datetime_format=True)
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df_test_1 = df_test
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df_test_1 = df_test_1.fillna(df_test.ffill())
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@ -194,9 +188,6 @@ def forecast_price():
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# 选择用于模型训练的列名称
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col_for_training = df_test_1.columns
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import joblib
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Best_model_DalyLGPrice = joblib.load("日度价格预测_液化气最佳模型.pkl")
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# 最新的一天为最后一行的数据
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@ -221,6 +212,8 @@ def forecast_price():
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a = round(a, 2)
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price_list.append(a)
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return a
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def optimize_Model():
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from sklearn.model_selection import train_test_split
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from sklearn.impute import SimpleImputer
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@ -234,10 +227,11 @@ def optimize_Model():
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df_test = pd.read_excel('液化气数据.xlsx')
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df_test.drop([0], inplace=True)
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try:
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df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)
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df_test['Date'] = pd.to_datetime(
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df_test['Date'], format='%m/%d/%Y', infer_datetime_format=True)
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except:
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df_test['Date']=pd.to_datetime(df_test['Date'], format=r'%Y-%m-%d',infer_datetime_format=True)
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df_test['Date'] = pd.to_datetime(
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df_test['Date'], format=r'%Y-%m-%d', infer_datetime_format=True)
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# 将缺失值补为前一个或者后一个数值
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df_test_1 = df_test
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@ -248,7 +242,6 @@ def optimize_Model():
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df_test_1 = df_test_1.drop(["Date"], axis=1)
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df_test_1 = df_test_1.astype('float')
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import numpy as np
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import pandas as pd
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from pandas import Series, DataFrame
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@ -297,7 +290,8 @@ def optimize_Model():
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target = y
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# 切割数据样本集合测试集
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X_train,x_test,y_train,y_true = train_test_split(train,target,test_size=0.2,random_state=0)
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X_train, x_test, y_train, y_true = train_test_split(
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train, target, test_size=0.2, random_state=0)
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# 模型缩写
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Lasso = Lasso(random_state=0)
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@ -328,14 +322,17 @@ def optimize_Model():
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model_results1
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# 定义plot_feature_importance函数，该函数用于计算特征重要性。此部分代码无需调整
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def plot_feature_importance(importance, names, model_type):
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feature_importance = np.array(importance)
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feature_names = np.array(names)
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data={'feature_names':feature_names,'feature_importance':feature_importance}
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data = {'feature_names': feature_names,
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'feature_importance': feature_importance}
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fi_df = pd.DataFrame(data)
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fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)
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fi_df.sort_values(by=['feature_importance'],
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ascending=False, inplace=True)
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plt.figure(figsize=(10, 8))
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sn.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])
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@ -345,7 +342,7 @@ def optimize_Model():
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plt.ylabel('FEATURE NAMES')
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from pylab import mpl
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mpl.rcParams['font.sans-serif'] = ['SimHei']
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## Xgboost 模型参数优化-初步
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# Xgboost 模型参数优化-初步
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# 参考: https://juejin.im/post/6844903661013827598
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# 每次调参时，备选参数数值以同数量级的1、3、10设置即可（比如设置1、3、10，或0.1、0.3、1.0，或0.01,0.03,0.10即可）
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@ -387,7 +384,8 @@ def optimize_Model():
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try:
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results = model_results1.append(model_results2, ignore_index=False)
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except:
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results = pd.concat([model_results1,model_results2],ignore_index=True)
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results = pd.concat(
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[model_results1, model_results2], ignore_index=True)
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import pickle
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Pkl_Filename = "日度价格预测_液化气最佳模型.pkl"
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@ -395,6 +393,7 @@ def optimize_Model():
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with open(Pkl_Filename, 'wb') as file:
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pickle.dump(grid_search_XGB, file)
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def read_xls_data():
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"""获取特征项ID"""
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global one_cols, two_cols
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@ -425,7 +424,8 @@ def start(date=''):
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print(data_value)
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dataItemNo_dataValue[data_value["dataItemNo"]] = ""
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else:
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dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
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dataItemNo_dataValue[data_value["dataItemNo"]
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] = data_value["dataValue"]
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for value in one_cols:
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if value in dataItemNo_dataValue:
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@ -472,6 +472,7 @@ def getNow(date='', offset=0):
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compact_date = date_str.replace("-", "")
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return compact_date, date_str
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def start_1(date=''):
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"""补充昨日数据"""
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read_xls_data()
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@ -493,7 +494,8 @@ def start_1(date=''):
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print(data_value)
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dataItemNo_dataValue[data_value["dataItemNo"]] = ""
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else:
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dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
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dataItemNo_dataValue[data_value["dataItemNo"]
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] = data_value["dataValue"]
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for value in one_cols:
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if value in dataItemNo_dataValue:
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@ -537,10 +539,12 @@ def save_xls_2(append_rows):
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except FileNotFoundError:
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# 如果文件不存在则创建新文件
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pd.DataFrame([append_rows]).to_excel('液化气数据.xlsx', index=False, engine='openpyxl')
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pd.DataFrame([append_rows]).to_excel(
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'液化气数据.xlsx', index=False, engine='openpyxl')
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except Exception as e:
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print(f"保存数据时发生错误: {str(e)}")
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def check_data(dataItemNo):
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token = get_head_auth()
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if not token:
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@ -550,6 +554,7 @@ def check_data(dataItemNo):
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if not datas:
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return
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def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd):
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search_data = {
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@ -563,13 +568,15 @@ def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEn
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}
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headers = {"Authorization": token}
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search_res = requests.post(url=url, headers=headers, json=search_data, timeout=(3, 5))
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search_res = requests.post(
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url=url, headers=headers, json=search_data, timeout=(3, 5))
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search_value = json.loads(search_res.text)["data"]
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if search_value:
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return search_value
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else:
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return None
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def save_queryDataListItemNos_xls(data_df, dataItemNoList):
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from datetime import datetime, timedelta
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current_year_month = datetime.now().strftime('%Y-%m')
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@ -624,7 +631,9 @@ def queryDataListItemNos(date=None,token=None):
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# 格式化为 YYYYMMDD 格式
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dateEnd = current_date.strftime('%Y%m%d')
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dateStart = first_day_of_month.strftime('%Y%m%d')
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search_value = get_queryDataListItemNos_value(token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd)
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# dateStart = '20250604'
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search_value = get_queryDataListItemNos_value(
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token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd)
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data_df = pd.DataFrame(search_value)
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data_df["dataDate"] = pd.to_datetime(data_df["dataDate"])
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data_df["dataDate"] = data_df["dataDate"].dt.strftime('%Y-%m-%d')
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@ -632,8 +641,6 @@ def queryDataListItemNos(date=None,token=None):
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print('当月数据更新完成')
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def main(start_date=None, token=None, token_push=None):
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from datetime import datetime, timedelta
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if start_date is None:
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@ -654,10 +661,18 @@ def main(start_date=None,token=None,token_push=None):
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# start(date)
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# 训练模型
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optimize_Model()
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# # 预测&上传预测结果
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# 预测&上传预测结果
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upload_data_to_system(token_push, start_date)
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# forecast_price()
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if __name__ == "__main__":
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print("运行中ing...")
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# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
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# for i_time in pd.date_range('2025-7-1', '2025-7-8', freq='D'):
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# # try:
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# print(i_time)
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# main(start_date=i_time)
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# except Exception as e:
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# continue
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main()
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