import xgboost as xgb
import pandas as pd
import os
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['font.sans-serif']=['kaiti']


pd.set_option('display.width',None)


df = pd.read_excel(r'C:\Users\鸽子\Desktop\入模数据\绍兴数据(1).xlsx')
df['dtdate'] = pd.to_datetime(df['dtdate'],format='%Y-%m-%d').astype('string')
df.set_index('dtdate',inplace=True)
plt.plot(range(len(df)),df['售电量'])
plt.show()
print(df.head())


df_eval = df[df.index.str[:7]=='2023-08']
# df_train = df[(df.index.str[:7]!='2023-08')&(df.index.str[:7]!='2023-09')]
df_train = df[400:850]
print(len(df_eval),len(df_train),len(df))



df_train = df_train[['tem_max','tem_min','holiday','24ST','rh','prs','售电量']]


# IQR = df['售电量'].describe()['75%'] - df['售电量'].describe()['25%']
# high = df['售电量'].describe()['75%'] + 1.5*IQR
# low = df['售电量'].describe()['25%'] - 1.5*IQR
# print('异常值数量：',len(df[(df['售电量'] >= high) | (df['售电量'] <= low)]))
#
# df_train = df_train[(df['售电量'] <= high) & (df['售电量'] >= low)]


X = df_train[['tem_max','tem_min','holiday','24ST','rh','prs']]
X_eval = df_eval[['tem_max','tem_min','holiday','24ST','rh','prs']]
y = df_train['售电量']

x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)
model = xgb.XGBRegressor(max_depth=6, learning_rate=0.05, n_estimators=150)
model.fit(x_train,y_train)


y_pred = model.predict(x_test)
result_test = pd.DataFrame({'test':y_test,'pred':y_pred},index=y_test.index)

# 指标打印
print(abs(y_test - y_pred).mean() / y_test.mean())
eval_pred = model.predict(X_eval)

result_eval = pd.DataFrame({'eval':df_eval['售电量'],'pred':eval_pred},index=df_eval['售电量'].index)
print(result_eval)
print((result_eval['eval'].sum()-result_eval['pred'].sum())/result_eval['eval'].sum())
print((result_eval['eval'].sum()-(result_eval['eval'][:-3].sum()+result_eval['pred'][-3:].sum()))/result_eval['eval'].sum())

result_eval.to_csv(r'C:\Users\鸽子\Desktop\各地市日电量预测结果\绍兴.csv')

# 保存模型
model.save_model('shaoxing.bin')
loaded_model = xgb.XGBRegressor()
loaded_model.load_model('shaoxing.bin')
model.predict(X_eval)