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import xgboost as xgb
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import pandas as pd
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import os
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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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import matplotlib as mpl
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import matplotlib.pyplot as plt
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mpl.rcParams['font.sans-serif']=['kaiti']
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pd.set_option('display.width',None)
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def season(x):
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if str(x)[5:7] in ('01', '10', '11'):
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return 0
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elif str(x)[5:7] in ('02', '03', '04', '05', '06', '09', '12'):
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return 1
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else:
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return 2
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def normal(nd):
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high = nd.describe()['75%'] + 1.5*(nd.describe()['75%']-nd.describe()['25%'])
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low = nd.describe()['25%'] - 1.5*(nd.describe()['75%']-nd.describe()['25%'])
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return nd[(nd<high)&(nd>low)]
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parent_dir = os.path.abspath(os.path.join(os.getcwd(),os.pardir))
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data = pd.read_excel(os.path.join(parent_dir,'入模数据/丽水.xlsx'),index_col='dtdate')
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data.index = pd.to_datetime(data.index,format='%Y-%m-%d')
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data = data.loc[normal(data['售电量']).index]
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# list2 = []
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# list0 = []
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# list1 = []
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# for i in ('01','02','03','04','05','06','07','08','09','10','11','12'):
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# month_index = data.index.strftime('%Y-%m-%d').str[5:7] == f'{i}'
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# print(data.loc[month_index]['售电量'].max(),data['售电量'].describe()['75%'])
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# if data.loc[month_index]['售电量'].mean() >= data['售电量'].describe()['75%']:
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# list2.append(i)
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# elif data.loc[month_index]['售电量'].mean() <= data['售电量'].describe()['25%']:
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# list0.append(i)
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# else:
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# list1.append(i)
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# print(list0,list1,list2)
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data['season'] = data.index.map(season)
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df_eval = data.loc['2023-9']
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# df_train = data.loc['2021-1':'2023-8']
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df_train = data[450:900]
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df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
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X = df_train[['tem_max','tem_min','holiday','24ST','season']]
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X_eval = df_eval[['tem_max','tem_min','holiday','24ST','season']]
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y = df_train['售电量']
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# best_goal = 1
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# best_i = {}
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# for i in range(200):
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x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)
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model = xgb.XGBRegressor(max_depth=6, learning_rate=0.05, n_estimators=150)
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model.fit(x_train,y_train)
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y_pred = model.predict(x_test)
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result_test = pd.DataFrame({'test':y_test,'pred':y_pred},index=y_test.index)
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# 指标打印
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print(abs(y_test - y_pred).mean() / y_test.mean())
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eval_pred = model.predict(X_eval)
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result_eval = pd.DataFrame({'eval':df_eval['售电量'],'pred':eval_pred},index=df_eval['售电量'].index)
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print(result_eval)
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goal = (result_eval['eval'][-3:].sum()-result_eval['pred'][-3:].sum())/result_eval['eval'].sum()
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print(goal)
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goal2 = (result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result_eval['eval'].sum()
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print(goal2)
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# if abs(goal) < best_goal:
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# best_goal = abs(goal)
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# best_i['best_i'] = i
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# print(best_i,best_goal)
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# with open(r'C:\Users\user\Desktop\9月各地市日电量预测结果\偏差率.txt','a',encoding='utf-8') as f:
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# f.write(f'丽水月末3天偏差率：{goal},9号-月底偏差率：{goal2}')
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# # 保存模型
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# model.save_model('lishui.bin')
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import numpy as np
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loaded_model = xgb.XGBRegressor()
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loaded_model.load_model('lishui.bin')
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X_eval = np.array([
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[22.3,16.19,23,1,0],
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[23.69,14.5,23,0,0],
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[23.69,14,23,0,0]])
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print(model.predict(X_eval))
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