718 lines
22 KiB
Python
718 lines
22 KiB
Python
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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from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
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import statsmodels.api as sm
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import datetime
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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 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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import xlrd
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import xlwt
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from datetime import datetime
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import time
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# 变量定义
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login_url = "http://10.200.32.39/jingbo-api/api/server/login"
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search_url = "http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos"
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queryDataListItemNos_url = "http://10.200.32.39/jingbo-api//api/warehouse/dwDataItem/queryDataListItemNos"
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login_push_url = "http://10.200.32.39/jingbo-api/api/server/login"
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upload_url = "http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList"
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login_data = {
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"data": {
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"account": "api_dev",
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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"terminal": "API"
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},
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"funcModule": "API",
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"funcOperation": "获取token"
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}
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login_push_data = {
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"data": {
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"account": "api_dev",
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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"terminal": "API"
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},
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"funcModule": "API",
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"funcOperation": "获取token"
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}
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read_file_path_name = "丙烯基础数据收集表.xls"
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one_cols = []
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two_cols = []
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# 导入机器学习算法模型
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# 切割训练数据和样本数据
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# 用于模型评分
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le = preprocessing.LabelEncoder()
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# print(__version__) # requires version >= 1.9.0
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cf.go_offline()
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random.seed(100)
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# 数据获取
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def get_head_auth():
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login_res = requests.post(url=login_url, json=login_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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return token
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else:
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print("获取认证失败")
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return None
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def get_data_value(token, dataItemNoList):
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search_data = {
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"data": {
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"date": get_cur_time()[0],
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"dataItemNoList": dataItemNoList
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},
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"funcModule": "数据项",
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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(
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url=search_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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print("今天没有新数据")
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return search_value
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# xls文件处理
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def write_xls(data):
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# 创建一个Workbook对象
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workbook = xlwt.Workbook()
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# 创建一个Sheet对象，可指定名称
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sheet = workbook.load('Sheet1')
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# 写入数据行
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for row_index, row_data in enumerate(data):
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for col_index, cell_data in enumerate(row_data):
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sheet.write(row_index, col_index, cell_data)
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# 保存Workbook到文件
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workbook.save(get_cur_time()[0] + '.xls')
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def get_cur_time():
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now = datetime.datetime.now()
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year = now.year
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month = now.month
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day = now.day
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if month < 10:
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month = "0" + str(month)
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if day < 10:
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day = "0" + str(day)
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cur_time = str(year) + str(month) + str(day)
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cur_time2 = str(year) + "-" + str(month) + "-" + str(day)
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# cur_time = '20231011'
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# cur_time2 = '2023-10-11'
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return cur_time, cur_time2
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def get_head_push_auth():
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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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return token
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else:
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print("获取认证失败")
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return None
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def upload_data_to_system(token_push):
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data = {
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"funcModule": "数据表信息列表",
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"funcOperation": "新增",
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"data": [
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{"dataItemNo": "C01100007|Forecast_Price|ACN",
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"dataDate": get_cur_time()[0],
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"dataStatus": "add",
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# "dataValue": 7100
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"dataValue": forecast_price()
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}
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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,
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json=data, timeout=(3, 5))
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print(res.text)
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# def upload_data_to_system(token):
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# data = {
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# "funcModule": "数据表信息列表",
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# "funcOperation": "新增",
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# "data": [
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# {"dataItemNo": "C01100036|Forecast_ Price|ACN",
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# "dataDate": '20230706',
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# "dataStatus": "add",
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# "dataValue": 3780.0
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# }
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# ]
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# }
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# headers = {"Authorization": token}
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# res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))
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# print(res.text)
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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('丙烯基础数据收集表.xlsx')
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df_test.drop([0], inplace=True)
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df_test['Date'] = pd.to_datetime(
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df_test['Date'], format='%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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df_test_1 = df_test_1.fillna(df_test.ffill())
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df_test_1 = df_test_1.fillna(df_test_1.bfill())
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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_丙烯最佳模型.pkl")
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# 最新的一天为最后一行的数据
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df_test_1_Day = df_test_1.tail(1)
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# 移除不需要的列
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df_test_1_Day.index = df_test_1_Day["Date"]
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df_test_1_Day = df_test_1_Day.drop(["Date"], axis=1)
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df_test_1_Day = df_test_1_Day.drop('Price', axis=1)
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df_test_1_Day = df_test_1_Day.dropna()
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# 转换数据类型
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df_test_1_Day = df_test_1_Day.astype(float)
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# df_test_1_Day
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# 预测今日价格，显示至小数点后两位
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Ypredict_Today = Best_model_DalyLGPrice.predict(df_test_1_Day)
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df_test_1_Day['日度预测价格'] = Ypredict_Today
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print(df_test_1_Day['日度预测价格'])
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a = df_test_1_Day['日度预测价格']
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a = a[0]
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a = float(a)
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a = round(a, 2)
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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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from sklearn.preprocessing import OrdinalEncoder
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from sklearn.feature_selection import SelectFromModel
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from sklearn.metrics import mean_squared_error, r2_score
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import pandas as pd
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pd.set_option('display.max_rows', 40)
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pd.set_option('display.max_columns', 40)
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df_test = pd.read_excel('丙烯基础数据收集表.xlsx')
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df_test.drop([0], inplace=True)
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df_test['Date'] = pd.to_datetime(
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df_test['Date'], format='%Y-%m-%d', infer_datetime_format=True)
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# 查看每个特征缺失值数量
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MisVal_Check = df_test.isnull().sum().sort_values(ascending=False)
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# 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
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df_MisVal_Check = pd.DataFrame(MisVal_Check,)
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df_MisVal_Check_1 = df_MisVal_Check.reset_index()
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df_MisVal_Check_1.columns = ['Variable_Name', 'Missing_Number']
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df_MisVal_Check_1['Missing_Number'] = df_MisVal_Check_1['Missing_Number'] / \
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len(df_test)
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df_test_1 = df_test.drop(
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df_MisVal_Check_1[df_MisVal_Check_1['Missing_Number'] > 0.4].Variable_Name, axis=1)
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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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df_test_1 = df_test_1.fillna(df_test.ffill())
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df_test_1 = df_test_1.fillna(df_test_1.bfill())
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df_test_1["Date"] = pd.to_datetime(df_test_1["Date"])
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df_test_1.index = df_test_1["Date"]
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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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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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from datetime import datetime
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import statsmodels.api as sm
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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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dataset1 = df_test_1.drop('Price', axis=1) # .astype(float)
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y = df_test_1['Price']
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x = dataset1
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train = x
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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(
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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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XGBR = XGBRegressor(random_state=0)
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# 训练模型
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Lasso.fit(X_train, y_train)
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XGBR.fit(X_train, y_train)
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# 模型拟合
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y_pre_Lasso = Lasso.predict(x_test)
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y_pre_XGBR = XGBR.predict(x_test)
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# 计算Lasso、XGBR、RandomForestR、AdaBoostR、GradientBoostingR、BaggingRegressor各模型的R²
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Lasso_score = r2_score(y_true, y_pre_Lasso)
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XGBR_score = r2_score(y_true, y_pre_XGBR)
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# 计算Lasso、XGBR的MSE和RMSE
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Lasso_MSE = mean_squared_error(y_true, y_pre_Lasso)
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XGBR_MSE = mean_squared_error(y_true, y_pre_XGBR)
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Lasso_RMSE = np.sqrt(Lasso_MSE)
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XGBR_RMSE = np.sqrt(XGBR_MSE)
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# 将不同模型的不同误差值整合成一个表格
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model_results = pd.DataFrame([['Lasso', Lasso_RMSE, Lasso_score],
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['XgBoost', XGBR_RMSE, XGBR_score]],
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columns=['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])
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# 将模型名称(Model)列设置为索引
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model_results1 = model_results.set_index('模型(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,
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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'],
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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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plt.title(model_type + " "+'FEATURE IMPORTANCE')
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plt.xlabel('FEATURE IMPORTANCE')
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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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# 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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from xgboost import XGBRegressor
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from sklearn.model_selection import GridSearchCV
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estimator = XGBRegressor(random_state=0,
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nthread=4,
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seed=0
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)
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parameters = {
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'max_depth': range(2, 11, 2), # 树的最大深度
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'n_estimators': range(50, 101, 10), # 迭代次数
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'learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]
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}
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grid_search_XGB = GridSearchCV(
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estimator=estimator,
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param_grid=parameters,
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# n_jobs = 10,
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cv=3,
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verbose=True
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)
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grid_search_XGB.fit(X_train, y_train)
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# 如果电脑在此步骤报错，可能是因为计算量太大，超过硬件可支持程度，可注释掉“n_jobs=10”一行
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best_parameters = grid_search_XGB.best_estimator_.get_params()
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y_pred = grid_search_XGB.predict(x_test)
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op_XGBR_score = r2_score(y_true, y_pred)
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op_XGBR_MSE = mean_squared_error(y_true, y_pred)
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op_XGBR_RMSE = np.sqrt(op_XGBR_MSE)
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model_results2 = pd.DataFrame([['Optimized_Xgboost', op_XGBR_RMSE, op_XGBR_score]],
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columns=['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])
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model_results2 = model_results2.set_index('模型(Model)')
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results = pd.concat([model_results1, model_results2], ignore_index=False)
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import pickle
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Pkl_Filename = "日度价格预测_丙烯最佳模型.pkl"
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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 queryDataListItemNos(token=None):
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df = pd.read_excel('丙烯基础数据收集表.xlsx')
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dataItemNoList = df.iloc[0].tolist()[1:]
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if token is None:
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token = get_head_auth()
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if not token:
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print('token获取失败')
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return
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# 获取当前日期
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from datetime import datetime, timedelta
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current_date = datetime.now()
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# 获取当月1日
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first_day_of_month = current_date.replace(day=1)
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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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# dateStart = '20241026'
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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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save_queryDataListItemNos_xls(data_df, dataItemNoList)
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print('当月数据更新完成')
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|
||
def save_queryDataListItemNos_xls(data_df, dataItemNoList):
|
||
from datetime import datetime, timedelta
|
||
current_year_month = datetime.now().strftime('%Y-%m')
|
||
grouped = data_df.groupby("dataDate")
|
||
|
||
# 使用openpyxl打开xlsx文件
|
||
from openpyxl import load_workbook
|
||
workbook = load_workbook('丙烯基础数据收集表.xlsx')
|
||
|
||
# 创建新工作簿
|
||
new_workbook = load_workbook('丙烯基础数据收集表.xlsx')
|
||
|
||
for sheetname in workbook.sheetnames:
|
||
sheet = workbook[sheetname]
|
||
new_sheet = new_workbook[sheetname]
|
||
|
||
current_year_month_row = 0
|
||
# 查找当前月份数据起始行
|
||
for row_idx, row in enumerate(sheet.iter_rows(values_only=True), 1):
|
||
if str(row[0]).startswith(current_year_month):
|
||
current_year_month_row += 1
|
||
|
||
# 追加新数据
|
||
if sheetname == workbook.sheetnames[0]:
|
||
start_row = sheet.max_row - current_year_month_row + 1
|
||
for row_idx, (date, group) in enumerate(grouped, start=start_row):
|
||
new_sheet.cell(row=row_idx, column=1, value=date)
|
||
for j, dataItemNo in enumerate(dataItemNoList, start=2):
|
||
if group[group["dataItemNo"] == dataItemNo]["dataValue"].values:
|
||
new_sheet.cell(row=row_idx, column=j,
|
||
value=group[group["dataItemNo"] == dataItemNo]["dataValue"].values[0])
|
||
|
||
# 保存修改后的xlsx文件
|
||
new_workbook.save("丙烯基础数据收集表.xlsx")
|
||
|
||
|
||
def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd):
|
||
|
||
search_data = {
|
||
"funcModule": "数据项",
|
||
"funcOperation": "查询",
|
||
"data": {
|
||
"dateStart": dateStart,
|
||
"dateEnd": dateEnd,
|
||
"dataItemNoList": dataItemNoList # 数据项编码,代表 brent最低价和最高价
|
||
}
|
||
}
|
||
|
||
headers = {"Authorization": token}
|
||
search_res = requests.post(
|
||
url=url, headers=headers, json=search_data, timeout=(3, 5))
|
||
search_value = json.loads(search_res.text)["data"]
|
||
if search_value:
|
||
return search_value
|
||
else:
|
||
return None
|
||
|
||
|
||
def read_xls_data():
|
||
global one_cols, two_cols
|
||
# 打开 XLS 文件
|
||
workbook = xlrd.open_workbook(read_file_path_name)
|
||
|
||
# 获取所有表格名称
|
||
# sheet_names = workbook.sheet_names()
|
||
|
||
# 选择第一个表格
|
||
sheet = workbook.sheet_by_index(0)
|
||
|
||
# 获取行数和列数
|
||
num_rows = sheet.nrows
|
||
# num_cols = sheet.ncols
|
||
|
||
# 遍历每一行，获取单元格数据
|
||
# for i in range(num_rows):
|
||
# row_data = sheet.row_values(i)
|
||
# one_cols.append(row_data)
|
||
# two_cols.append(row_data[1])
|
||
|
||
row_data = sheet.row_values(1)
|
||
one_cols = row_data
|
||
|
||
# 关闭 XLS 文件
|
||
# workbook.close()
|
||
|
||
|
||
def start():
|
||
read_xls_data()
|
||
|
||
token = get_head_auth()
|
||
if not token:
|
||
return
|
||
token_push = get_head_push_auth()
|
||
if not token_push:
|
||
return
|
||
|
||
datas = get_data_value(token, one_cols[1:])
|
||
# if not datas:
|
||
# return
|
||
|
||
# data_list = [two_cols, one_cols]
|
||
append_rows = [get_cur_time()[1]]
|
||
dataItemNo_dataValue = {}
|
||
for data_value in datas:
|
||
if "dataValue" not in data_value:
|
||
print(data_value)
|
||
dataItemNo_dataValue[data_value["dataItemNo"]] = ""
|
||
else:
|
||
dataItemNo_dataValue[data_value["dataItemNo"]
|
||
] = data_value["dataValue"]
|
||
|
||
for value in one_cols[1:]:
|
||
if value in dataItemNo_dataValue:
|
||
append_rows.append(dataItemNo_dataValue[value])
|
||
else:
|
||
append_rows.append("")
|
||
save_xls(append_rows)
|
||
optimize_Model()
|
||
upload_data_to_system(token_push)
|
||
# data_list.append(three_cols)
|
||
# write_xls(data_list)
|
||
|
||
|
||
def start_1():
|
||
read_xls_data()
|
||
|
||
token = get_head_auth()
|
||
if not token:
|
||
return
|
||
|
||
datas = get_data_value(token, one_cols[1:])
|
||
# if not datas:
|
||
# return
|
||
|
||
# data_list = [two_cols, one_cols]
|
||
append_rows = [get_cur_time()[1]]
|
||
dataItemNo_dataValue = {}
|
||
for data_value in datas:
|
||
if "dataValue" not in data_value:
|
||
print(data_value)
|
||
dataItemNo_dataValue[data_value["dataItemNo"]] = ""
|
||
else:
|
||
dataItemNo_dataValue[data_value["dataItemNo"]
|
||
] = data_value["dataValue"]
|
||
|
||
for value in one_cols[1:]:
|
||
if value in dataItemNo_dataValue:
|
||
append_rows.append(dataItemNo_dataValue[value])
|
||
else:
|
||
append_rows.append("")
|
||
save_xls_1(append_rows)
|
||
|
||
# data_list.append(three_cols)
|
||
# write_xls(data_list)
|
||
|
||
|
||
def save_xls_1(append_rows):
|
||
|
||
# 打开xls文件
|
||
workbook = xlrd.open_workbook('丙烯基础数据收集表.xls')
|
||
|
||
# 获取所有sheet的个数
|
||
sheet_count = len(workbook.sheet_names())
|
||
|
||
# 获取所有sheet的名称
|
||
sheet_names = workbook.sheet_names()
|
||
|
||
new_workbook = xlwt.Workbook()
|
||
for i in range(sheet_count):
|
||
# 获取当前sheet
|
||
sheet = workbook.sheet_by_index(i)
|
||
|
||
# 获取sheet的行数和列数
|
||
row_count = sheet.nrows - 1
|
||
col_count = sheet.ncols
|
||
# 获取原有数据
|
||
data = []
|
||
for row in range(row_count):
|
||
row_data = []
|
||
for col in range(col_count):
|
||
row_data.append(sheet.cell_value(row, col))
|
||
data.append(row_data)
|
||
# 创建xlwt的Workbook对象
|
||
# 创建sheet
|
||
new_sheet = new_workbook.add_sheet(sheet_names[i])
|
||
|
||
# 将原有的数据写入新的sheet
|
||
for row in range(row_count):
|
||
for col in range(col_count):
|
||
new_sheet.write(row, col, data[row][col])
|
||
|
||
if i == 0:
|
||
# 在新的sheet中添加数据
|
||
for col in range(col_count):
|
||
new_sheet.write(row_count, col, append_rows[col])
|
||
|
||
# 保存新的xls文件
|
||
new_workbook.save("丙烯基础数据收集表.xls")
|
||
|
||
|
||
def check_data(dataItemNo):
|
||
token = get_head_auth()
|
||
if not token:
|
||
return
|
||
|
||
datas = get_data_value(token, dataItemNo)
|
||
if not datas:
|
||
return
|
||
|
||
|
||
def save_xls(append_rows):
|
||
|
||
# 打开xls文件
|
||
workbook = xlrd.open_workbook('丙烯基础数据收集表.xls')
|
||
|
||
# 获取所有sheet的个数
|
||
sheet_count = len(workbook.sheet_names())
|
||
|
||
# 获取所有sheet的名称
|
||
sheet_names = workbook.sheet_names()
|
||
|
||
new_workbook = xlwt.Workbook()
|
||
for i in range(sheet_count):
|
||
# 获取当前sheet
|
||
sheet = workbook.sheet_by_index(i)
|
||
|
||
# 获取sheet的行数和列数
|
||
row_count = sheet.nrows
|
||
col_count = sheet.ncols
|
||
# 获取原有数据
|
||
data = []
|
||
for row in range(row_count):
|
||
row_data = []
|
||
for col in range(col_count):
|
||
row_data.append(sheet.cell_value(row, col))
|
||
data.append(row_data)
|
||
# 创建xlwt的Workbook对象
|
||
# 创建sheet
|
||
new_sheet = new_workbook.add_sheet(sheet_names[i])
|
||
|
||
# 将原有的数据写入新的sheet
|
||
for row in range(row_count):
|
||
for col in range(col_count):
|
||
new_sheet.write(row, col, data[row][col])
|
||
|
||
if i == 0:
|
||
# 在新的sheet中添加数据
|
||
for col in range(col_count):
|
||
new_sheet.write(row_count, col, append_rows[col])
|
||
|
||
# 保存新的xls文件
|
||
new_workbook.save("丙烯基础数据收集表.xls")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# start()
|
||
queryDataListItemNos()
|
||
optimize_Model()
|
||
forecast_price()
|
||
upload_data_to_system(token_push=get_head_push_auth())
|