CCF乘用车细分市场销量预测baseline(0.488)

import numpy as np
import pandas as pd
import os 
import gc
from tqdm import tqdm, tqdm_notebook
from sklearn.model_selection import StratifiedKFold, KFold
from sklearn.metrics import f1_score, roc_auc_score
import datetime
import time
import lightgbm as lgb
import xgboost as xgb

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.filterwarnings('ignore')

path  = './ccf_car/'

train_sales  = pd.read_csv(path+'train_sales_data.csv')
train_search = pd.read_csv(path+'train_search_data.csv')
train_user   = pd.read_csv(path+'train_user_reply_data.csv')

evaluation_public = pd.read_csv(path+'evaluation_public.csv')
submit_example    = pd.read_csv(path+'submit_example.csv')


# 2018年1月，提取方式历史月份销量比例，考虑时间衰减，月份越近占比越高，即考虑同比与环比
m1_12    = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==12), 'salesVolume'].values
m1_11    = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==11), 'salesVolume'].values
m1_10    = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==10), 'salesVolume'].values
m1_09    = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==9) , 'salesVolume'].values
m1_08    = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==8) , 'salesVolume'].values

m1_12_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==12), 'salesVolume'].values * m1_12
m1_11_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==11), 'salesVolume'].values * m1_11
m1_10_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==10), 'salesVolume'].values * m1_10
m1_09_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==9) , 'salesVolume'].values * m1_09
m1_08_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==8) , 'salesVolume'].values * m1_08

evaluation_public.loc[evaluation_public.regMonth==1, 'forecastVolum'] =  m1_12_volum/2 + m1_11_volum/4 + m1_10_volum/8 + m1_09_volum/16 + m1_08_volum/16

# 2018年1、2、3月，提取方式历史月份销量比例，考虑时间衰减，月份越近占比越高
m16_1_2  = train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==2) , 'salesVolume'].values
m16_1_3  = train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==3) , 'salesVolume'].values
m16_1_4  = train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==4) , 'salesVolume'].values
m16_1_5  = train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==5) , 'salesVolume'].values

m16_2_3  = train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==3) , 'salesVolume'].values
m16_2_4  = train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==4) , 'salesVolume'].values
m16_2_5  = train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==5) , 'salesVolume'].values
m16_2_6  = train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==6) , 'salesVolume'].values

m16_3_4  = train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==4) , 'salesVolume'].values
m16_3_5  = train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==5) , 'salesVolume'].values
m16_3_6  = train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==6) , 'salesVolume'].values
m16_3_7  = train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==7) , 'salesVolume'].values

m17_1_2  = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2) , 'salesVolume'].values
m17_1_3  = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3) , 'salesVolume'].values
m17_1_4  = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==4) , 'salesVolume'].values
m17_1_5  = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==5) , 'salesVolume'].values


m17_2_3  = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3) , 'salesVolume'].values
m17_2_4  = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==4) , 'salesVolume'].values
m17_2_5  = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==5) , 'salesVolume'].values
m17_2_6  = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==6) , 'salesVolume'].values

m17_3_4  = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==4) , 'salesVolume'].values
m17_3_5  = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==5) , 'salesVolume'].values
m17_3_6  = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==6) , 'salesVolume'].values
m17_3_7  = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==7) , 'salesVolume'].values

m16_1 = m16_1_2/2 + m16_1_3/4 + m16_1_4/8 + m16_1_5/8
m16_2 = m16_2_3/2 + m16_2_4/4 + m16_2_5/8 + m16_2_6/8
m16_3 = m16_3_4/2 + m16_3_5/4 + m16_3_6/8 + m16_3_7/8

m17_1 = m17_1_2/2 + m17_1_3/4 + m17_1_4/8 + m17_1_5/8
m17_2 = m17_2_3/2 + m17_2_4/4 + m17_2_5/8 + m17_2_6/8
m17_3 = m17_3_4/2 + m17_3_5/4 + m17_3_6/8 + m17_3_7/8

m1 = m16_1 * 0.4 +  m17_1 * 0.6
m2 = m16_2 * 0.4 +  m17_2 * 0.6
m3 = m16_3 * 0.4 +  m17_3 * 0.6

evaluation_public.loc[evaluation_public.regMonth==2, 'forecastVolum'] = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1) , 'salesVolume'].values / m1
evaluation_public.loc[evaluation_public.regMonth==3, 'forecastVolum'] = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2) , 'salesVolume'].values / m2
evaluation_public.loc[evaluation_public.regMonth==4, 'forecastVolum'] = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3) , 'salesVolume'].values / m3

evaluation_public[['id','forecastVolum']].round().astype(int).to_csv('ccf_car_sales.csv', index=False)