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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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def season(x):
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if str(x)[5:7] in ('06','07','08','12','01','02'):
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return 1
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else:
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return 0
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mpl.rcParams['font.sans-serif']=['kaiti']
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pd.set_option('display.width',None)
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data = pd.read_excel(r'C:\Users\user\PycharmProjects\pytorch2\入模数据\台州数据(1).xlsx',index_col='dtdate')
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data.index = pd.to_datetime(data.index,format='%Y-%m-%d')
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data['season'] = data.index.map(season)
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# plt.plot(range(len(data)),data['售电量'])
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# plt.show()
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print(data.head())
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df_eval = data.loc['2023-8']
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# df_train = data.loc['2021-1':'2023-7']
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df_train = data[500:850]
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print(len(df_eval),len(df_train),len(data))
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df_train = df_train[['tem_max','tem_min','holiday','24ST','rh','prs','售电量','season']]
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# IQR = df['售电量'].describe()['75%'] - df['售电量'].describe()['25%']
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# high = df['售电量'].describe()['75%'] + 1.5*IQR
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# low = df['售电量'].describe()['25%'] - 1.5*IQR
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# print('异常值数量：',len(df[(df['售电量'] >= high) | (df['售电量'] <= low)]))
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#
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# df_train = df_train[(df['售电量'] <= high) & (df['售电量'] >= low)]
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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(400):
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x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=163)
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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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# print((result_eval['eval'].sum()-result_eval['pred'].sum())/result_eval['eval'].sum())
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print((result_eval['eval'][-3:].sum()-result_eval['pred'][-3:].sum())/result_eval['eval'].sum())
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goal = (result_eval['eval'][-3:].sum()-result_eval['pred'][-3:].sum())/result_eval['eval'].sum()
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print((result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result_eval['eval'].sum())
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goal2 = (result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result_eval['eval'].sum()
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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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#
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# result_eval.to_csv(r'C:\Users\user\Desktop\9月各地市日电量预测结果\台州.csv')
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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'台州月末3天偏差率：{round(goal,5)},9号-月底偏差率：{round(goal2,5)}\n')
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# # 保存模型
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# model.save_model('taizhou.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('taizhou.bin')
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X_eval = np.array([[25.1,16.8,23,0,0],
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[22.8,16.3,23,1,0],
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[22.7,14.6,23,1,0],
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[22.5,14.4,23,0,0],
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[22.6,15.6,23,0,0]])
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print(model.predict(X_eval))
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