import pandas as pd
import matplotlib.pyplot as plt
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score


df = pd.read_excel('../400v入模数据/丽水.xlsx',index_col='stat_date')

df.index = pd.to_datetime(df.index)

x_train = df.loc['2021-1':'2023-7'][:-3].drop(columns='0.4kv及以下')
y_train = df.loc['2021-1':'2023-7'][:-3]['0.4kv及以下']
x_eval = df.loc['2023-7'].drop(columns='0.4kv及以下')
y_eval = df.loc['2023-7']['0.4kv及以下']

x_train,x_test,y_train,y_test = train_test_split(x_train,y_train,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)
print(r2_score(y_test,y_pred))

predict = model.predict(x_eval)
result = pd.DataFrame({'eval':y_eval,'pred':predict},index=y_eval.index)
print(result)
print((result['eval'][-3:].sum()-result['pred'][-3:].sum())/result['eval'].sum())


import numpy as np

X_eval = np.array([
                   [22.3,16.19,23,1,0],
                       [23.69,14.5,23,0,0],
                       [23.69,14,23,0,0]])
print(model.predict(X_eval))