648 lines
20 KiB
Python
648 lines
20 KiB
Python
# coding: utf-8
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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 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, timedelta
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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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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 = "液化气数据.xls"
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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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# 导入机器学习算法模型
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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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# 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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print('获取的token:', token)
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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, date):
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search_data = {
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"data": {
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"date": date,
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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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try:
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search_value = json.loads(search_res.text)["data"]
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except json.JSONDecodeError as e:
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print(f"Error decoding JSON: {e}")
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print("Response content:", search_res.text)
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return None
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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 get_cur_time(date=''):
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if date == '':
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now = datetime.now()
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else:
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now = date
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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, date):
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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": "250855713|Forecast_Price|ACN",
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"dataDate": date,
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"dataStatus": "add",
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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, 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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def getLogToken():
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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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else:
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print("获取认证失败")
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token = None
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return token
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def updateYesterdayExcelData(date='', token=None):
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# 使用pandas读取Excel文件
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df = pd.read_excel(read_file_path_name, engine='openpyxl')
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# 获取第二行的数据作为列名
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one_cols = df.iloc[0, :].tolist()
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# 获取当前日期的前一天
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if date == '':
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previous_date = (datetime.now() - timedelta(days=1)
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).strftime('%Y-%m-%d')
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else:
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# 字符串转日期
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previous_date = (datetime.strptime(date, "%Y-%m-%d") -
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timedelta(days=1)).strftime('%Y-%m-%d')
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cur_time, cur_time2 = getNow(previous_date)
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search_data = {
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"data": {
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"date": cur_time,
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"dataItemNoList": one_cols[1:]
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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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print('数据请求结果：')
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print(search_res.text)
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search_value = json.loads(search_res.text)["data"]
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if search_value:
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datas = search_value
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else:
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datas = None
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append_rows = [cur_time2]
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dataItemNo_dataValue = {}
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for data_value in datas:
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if "dataValue" not in data_value:
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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"]
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] = data_value["dataValue"]
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for value in one_cols[1:]:
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if value in dataItemNo_dataValue:
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append_rows.append(dataItemNo_dataValue[value])
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else:
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append_rows.append("")
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print('更新数据前')
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print(df.tail(1))
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# 检查日期是否已存在于数据中
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if previous_date not in df['日期'].values:
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# 将新的数据添加到DataFrame中
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new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())
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df = pd.concat([df, new_row], ignore_index=True)
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else:
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# 更新现有数据
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print('日期存在，即将更新')
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print('新数据', append_rows[1:])
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df.loc[df['日期'] == previous_date,
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df.columns.tolist()[1:]] = append_rows[1:]
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print('更新数据后')
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print(df.tail(1))
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# 使用pandas保存Excel文件
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df.to_excel("液化气数据.xls", index=False, engine='openpyxl')
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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.xls')
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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(
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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(
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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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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_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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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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for col in df_test_1_Day.columns:
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df_test_1_Day[col] = pd.to_numeric(df_test_1_Day[col], errors='coerce')
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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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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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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('液化气数据.xls')
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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(
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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(
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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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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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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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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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# 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)
|
||
op_XGBR_RMSE = np.sqrt(op_XGBR_MSE)
|
||
|
||
model_results2 = pd.DataFrame([['Optimized_Xgboost', op_XGBR_RMSE, op_XGBR_score]],
|
||
columns=['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])
|
||
model_results2 = model_results2.set_index('模型(Model)')
|
||
|
||
try:
|
||
results = model_results1.append(model_results2, ignore_index=False)
|
||
except:
|
||
results = pd.concat(
|
||
[model_results1, model_results2], ignore_index=True)
|
||
import pickle
|
||
|
||
Pkl_Filename = "日度价格预测_液化气最佳模型.pkl"
|
||
|
||
with open(Pkl_Filename, 'wb') as file:
|
||
pickle.dump(grid_search_XGB, file)
|
||
|
||
|
||
def read_xls_data():
|
||
global one_cols, two_cols
|
||
# 使用pandas读取Excel文件
|
||
df = pd.read_excel(read_file_path_name, header=None) # 不自动识别列名
|
||
# 获取第二行数据（索引为1）
|
||
one_cols = df.iloc[1].tolist()[1:]
|
||
|
||
|
||
def start(date=''):
|
||
read_xls_data()
|
||
|
||
token = get_head_auth()
|
||
if not token:
|
||
return
|
||
token_push = get_head_push_auth()
|
||
if not token_push:
|
||
return
|
||
cur_time, cur_time2 = getNow(date)
|
||
datas = get_data_value(token, one_cols, cur_time)
|
||
# if not datas:
|
||
# return
|
||
|
||
# data_list = [two_cols, one_cols]
|
||
append_rows = [cur_time2]
|
||
dataItemNo_dataValue = {}
|
||
for data_value in datas:
|
||
if "dataValue" not in data_value:
|
||
dataItemNo_dataValue[data_value["dataItemNo"]] = ""
|
||
else:
|
||
dataItemNo_dataValue[data_value["dataItemNo"]
|
||
] = data_value["dataValue"]
|
||
|
||
for value in one_cols:
|
||
if value in dataItemNo_dataValue:
|
||
append_rows.append(dataItemNo_dataValue[value])
|
||
else:
|
||
append_rows.append("")
|
||
save_xls_2(append_rows)
|
||
optimize_Model()
|
||
upload_data_to_system(token_push, cur_time)
|
||
|
||
# data_list.append(three_cols)
|
||
# write_xls(data_list)
|
||
|
||
|
||
def getNow(date='', offset=0):
|
||
|
||
if date == '':
|
||
now = datetime.now() - timedelta(days=offset)
|
||
else:
|
||
if isinstance(date, datetime):
|
||
now = date
|
||
else:
|
||
try:
|
||
# 先尝试常见日期格式解析
|
||
now = datetime.strptime(str(date), "%Y-%m-%d")
|
||
except ValueError:
|
||
# 失败后尝试无分隔符格式
|
||
now = datetime.strptime(str(date), "%Y%m%d")
|
||
now = now - timedelta(days=offset)
|
||
year = now.year
|
||
month = now.month
|
||
day = now.day
|
||
|
||
if month < 10:
|
||
month = "0" + str(month)
|
||
if day < 10:
|
||
day = "0" + str(day)
|
||
cur_time = str(year) + str(month) + str(day)
|
||
cur_time2 = str(year) + "-" + str(month) + "-" + str(day)
|
||
return cur_time, cur_time2
|
||
|
||
|
||
def start_1(date=''):
|
||
read_xls_data()
|
||
token = get_head_auth()
|
||
if not token:
|
||
return
|
||
|
||
cur_time, cur_time2 = getNow(date, offset=1)
|
||
print(f"补充{cur_time}数据")
|
||
datas = get_data_value(token, one_cols, date=cur_time)
|
||
# if not datas:
|
||
# return
|
||
|
||
# data_list = [two_cols, one_cols]
|
||
append_rows = [cur_time2]
|
||
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:
|
||
if value in dataItemNo_dataValue:
|
||
append_rows.append(dataItemNo_dataValue[value])
|
||
else:
|
||
append_rows.append("")
|
||
save_xls_2(append_rows)
|
||
|
||
# data_list.append(three_cols)
|
||
# write_xls(data_list)
|
||
|
||
|
||
def save_xls_2(append_rows):
|
||
try:
|
||
# 读取现有数据（假设第一行为列名）
|
||
df = pd.read_excel('液化气数据.xls', sheet_name=0)
|
||
append_rows = pd.DataFrame(append_rows, index=df.columns).T
|
||
# 创建新数据行
|
||
new_date = append_rows['Date'].values[0]
|
||
|
||
dates = df['Date'].to_list()
|
||
|
||
# 判断日期是否存在
|
||
if new_date in dates:
|
||
# 找到日期所在行的索引
|
||
date_mask = df['Date'] == new_date
|
||
# 存在则更新数据
|
||
df.loc[date_mask] = append_rows.values
|
||
print(f"更新 {new_date} 数据")
|
||
else:
|
||
# 不存在则追加数据
|
||
df = pd.concat([df, append_rows], ignore_index=True)
|
||
print(f"追加 {new_date} 数据")
|
||
|
||
# 保存更新后的数据
|
||
df.to_excel('液化气数据.xls', index=False, engine='openpyxl')
|
||
|
||
except FileNotFoundError:
|
||
# 如果文件不存在则创建新文件
|
||
pd.DataFrame([append_rows]).to_excel(
|
||
'液化气数据.xls', index=False, engine='openpyxl')
|
||
# except Exception as e:
|
||
# print(f"保存数据时发生错误: {str(e)}")
|
||
|
||
|
||
start_date = datetime(2025, 3, 10)
|
||
end_date = datetime(2025, 3, 20)
|
||
|
||
token = getLogToken()
|
||
while start_date < end_date:
|
||
date = start_date.strftime('%Y-%m-%d')
|
||
updateYesterdayExcelData(date, token=token)
|
||
# start(date)
|
||
# # time.sleep(1)
|
||
# start_1(start_date)
|
||
# start_date += timedelta(days=1)
|
||
time.sleep(5)
|