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)

def hf_season(x):
    list1= []
    for i in range(1,13):
        if x.loc[f'2021-{i}'].mean() >= x.describe()['75%']:
            list1.append(i)
    return list1


def season(x):
    if str(x)[5:7] in ('06','07','08','12','01','02'):
        return 1
    else:
        return 0
def month(x):
    if str(x)[5:7] in ('08','09','10','12','01','02'):
        return 1
    else:
        return 0
def normal(nd):
    high = nd.describe()['75%'] + 1.5*(nd.describe()['75%']-nd.describe()['25%'])
    low = nd.describe()['25%'] - 1.5*(nd.describe()['75%']-nd.describe()['25%'])
    return nd[(nd<high)&(nd>low)]


data = pd.read_excel(r'C:\python-project\pytorch3\入模数据\杭州数据.xlsx',index_col='dtdate')
data.index = pd.to_datetime(data.index,format='%Y-%m-%d')
data = data.loc[normal(data['售电量']).index]
# for i in range(1,13):
# plt.plot(range(len(data['售电量'][f'2022-{i}'])),data['售电量'][f'2022-{i}'])
# plt.show()
print(data['售电量']['2022-9'])
plt.plot(range(len(data['售电量']['2022-7'])),data['售电量']['2022-7'])
plt.plot(range(len(data['售电量']['2022-7']),len(data['售电量']['2022-7'])+len(data['售电量']['2023-7'])),data['售电量']['2023-7'])
# plt.plot(range(len(data['售电量'][['2022-9','2023-9']])),data['售电量'][['2022-9','2023-9']])
plt.show()

# print(hf_season(data.loc['2021']['售电量']))

data['month'] = data.index.strftime('%Y-%m-%d').str[6]
data['month'] = data['month'].astype('int')
data['season'] = data.index.map(season)
print(data.head(50))

df_eval = data.loc['2023-7']
df_train = data.loc['2021-1':'2023-6']
# df_train = df[500:850]
print(len(df_eval),len(df_train),len(data))

print(data.drop(columns='city_name').corr(method='pearson')['售电量'])

df_train = df_train[['tem_max','tem_min','24ST','rh','rh_max','prs','prs_max','prs_min','售电量','month','holiday','season']]


# 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','24ST','holiday','season']]
X_eval = df_eval[['tem_max','tem_min','24ST','holiday','season']]
y = df_train['售电量']
print(y.describe())
# best_goal = 1
# best_i = {}
# for i in range(400):

x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.15,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['eval'].sum()-result_eval['pred'].sum())/result_eval['eval'].sum())

goal = (result_eval['eval'][-3:].sum()-result_eval['pred'][-3:].sum())/result_eval['eval'].sum()
print('goal:',goal)

goal2 = (result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result_eval['eval'].sum()

print('goal2:',goal2)
print(result_eval)
print('r2:',r2_score(y_test,y_pred))
    # if abs(goal) < best_goal:
    #     best_goal = abs(goal)
    #     best_i['best_i'] = i
    #     x = goal2
# print(best_i,best_goal,x)



# result_eval.to_csv(r'C:\Users\user\Desktop\9月各地市日电量预测结果\杭州.csv')
# 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:
#     f.write(f'杭州月末3天偏差率：{round(goal,5)},9号-月底偏差率：{round(goal2,5)}\n')
# # 保存模型
# model.save_model('hangzhou.bin')
# loaded_model = xgb.XGBRegressor()
# loaded_model.load_model('hangzhou.bin')
# model.predict(X_eval)