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 season(x):
    if str(x)[5:7] in ('01', '02', '10'):
        return 0
    elif str(x)[5:7] in ('03', '04', '05', '06', '11', '12'):
        return 1
    else:
        return 2


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/温州.xlsx'), index_col='dtdate')
data.index = pd.to_datetime(data.index, format='%Y-%m-%d')
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['season'] = data.index.map(season)

# data = data.loc[:'2023-8']
df_eval = data.loc['2023-11']

df_train = data[450:]
# df_train = data[450:][:-3]
print(df_train)

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

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']

# 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=0.1, 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)

goal2 = (result_eval['eval'][-23:].sum() - result_eval['pred'][-23:].sum()) / result_eval['eval'].sum()
print(goal2)
print(result_eval)
# if abs(goal) < best_goal :
#     best_goal = abs(goal)
#     best_i['best_i'] = i
#     x = goal2
# print(best_i,best_goal,x)
# 保存模型
model.save_model('wenzhou.bin')

loaded_model = xgb.XGBRegressor()

loaded_model.load_model('wenzhou.bin')
import numpy as np

X_eval = np.array([
    [19.8, 6.6, 10, 0, 1],
    [22, 6.1, 10, 0, 1],
    [18.5, 10.1, 10, 0, 1]
])
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)