import xgboost as xgb
import pandas as pd
import numpy as np
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 season(x):
    if str(x)[5:7] in ('04', '10'):
        return 0
    elif str(x)[5:7] in ('01', '02', '03', '05', '06', '09', '11', '12'):
        return 1
    else:
        return 2


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)]


parent_dir = os.path.abspath(os.path.join(os.getcwd(), os.pardir))
data = pd.read_excel(os.path.join(parent_dir, '入模数据/杭州.xlsx'))
data['dtdate'] = pd.to_datetime(data['dtdate'],format='%Y-%m-%d')
data['year'] = data['dtdate'].dt.year
# data.index = pd.to_datetime(data.index, format='%Y-%m-%d')
data.set_index('dtdate',inplace=True)
data = data.loc[normal(data['售电量']).index]

# list2 = []
# list0 = []
# list1 = []
# for i in ('01','02','03','04','05','06','07','08','09','10','11','12'):
#     month_index = data.index.strftime('%Y-%m-%d').str[5:7] == f'{i}'
#     if data.loc[month_index]['售电量'].mean() >= data['售电量'].describe()['75%']:
#         list2.append(i)
#     elif data.loc[month_index]['售电量'].mean() <= data['售电量'].describe()['25%']:
#         list0.append(i)
#     else:
#         list1.append(i)
# print(list0,list1,list2)


# data['month'] = data.index.strftime('%Y-%m-%d').str[6]
# data['month'] = data['month'].astype('int')
data['season'] = data.index.map(season)
# data = data.loc[:'2023-9']
df_train = data[500:-1]
# df_train = data[500:][:-3]
df_eval = data.loc['2023-11']

print(df_train)
X = df_train[['tem_max', 'tem_min', '24ST', 'holiday', 'season','year']]
X_eval = df_eval[['tem_max', 'tem_min', '24ST', 'holiday', 'season','year']]
y = df_train['售电量']

# 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=0.2, 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)

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))
#
# # result_eval.to_csv('asda.csv',encoding='gbk')
#     # 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')

# X_eval = df_eval[['tem_max','tem_min','24ST','holiday','season']]
# df_eval = pd.read_excel(r'C:\Users\user\Desktop\浙江气象1027.xlsx')
# df_eval.columns = df_eval.columns.map(lambda x:x.strip())
# df_eval = df_eval[['city_name','dtdate','tem_max','tem_min']]
# df_eval['city_name'] = df_eval['city_name'].map(lambda x:x.strip())
# df_hangzhou = df_eval[df_eval['city_name']=='金华市'].sort_values(by='dtdate')


loaded_model = xgb.XGBRegressor()
loaded_model.load_model('hangzhou.bin')
X_eval = np.array([
    [17.2, 5.7, 10, 0, 0,2023],
    [21.2, 4.3, 10, 0, 0,2023],
    [11.5, 6.6, 10, 0, 0,2023]
])
print(model.predict(X_eval))
result = model.predict(X_eval)
result = pd.DataFrame(result,index=['2023-11-28','2023-11-29','2023-11-30'])
result = pd.concat((result_eval['eval'],result))
result.index = result.index.map(lambda x:str(x)[:10])
print(result)