赛题地址：http://challenge.xfyun.cn/topic/info?type=temperature
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 30 17:00:08 2020
@author: csdn lanxuxml
"""
import pandas as pd
train_data = pd.read_csv('train.csv')
testA_data = pd.read_csv('test.csv')
sub = pd.DataFrame(testA_data['time'])
train_data = train_data[train_data['temperature'].notnull()]
train_data = train_data.fillna(method='bfill')
testA_data = testA_data.fillna(method='bfill')
testA_data.columns = ['time','year','month','day','hour','min','sec','outdoorTemp','outdoorHum','outdoorAtmo','indoorHum','indoorAtmo']
train_data.columns = ['time','year','month','day','hour','min','sec','outdoorTemp','outdoorHum','outdoorAtmo','indoorHum','indoorAtmo', 'temperature']
train_data = train_data[['hour','min','sec','outdoorTemp','outdoorHum','outdoorAtmo','indoorHum','indoorAtmo', 'temperature']]
testA_data = testA_data[['hour','min','sec','outdoorTemp','outdoorHum','outdoorAtmo','indoorHum','indoorAtmo']]
train_data['ratio_outdoorTemp'] = 1/train_data['outdoorTemp']
testA_data['ratio_outdoorTemp'] = 1/testA_data['outdoorTemp']
train_data['temperature-rewise'] = train_data['temperature'] - train_data['outdoorTemp']
train_data.drop(['temperature'], axis=1, inplace=True)
from pycaret.regression import *
exp_reg = setup(train_data, target = 'temperature-rewise', categorical_features = ['hour','min','sec'], train_size = 0.85)
lr=create_model('lr')
evaluate_model(lr)
pred_holdout = predict_model(lr)
lr_final = finalize_model(lr)
pred_testA = predict_model(lr_final, data=testA_data)
sub["temperature"]=pred_testA['Label'] + testA_data['outdoorTemp']
sub.to_csv("submision_xunfei.csv",index=False)