写在前面
本文只是一个基本的统计策略baseline，分数一般，可以继续优化或者作为融合的一部分。
不过与你们手上的模型结果直接加权融合，应该会有很大的提升。
比赛官网：
赛题详情(Competition Details) - DataFountainwww.datafountain.cn
完整代码
代码语言：javascript复制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')
##### 1月
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_07 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==7) , 'salesVolume'].values
m1_06 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==6) , '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
m1_07_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==7) , 'salesVolume'].values * m1_07
m1_06_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==6) , 'salesVolume'].values * m1_06
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/32 m1_07_volum/64 m1_06_volum/64
##### 2月
m2_01 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1 ) , 'salesVolume'].values
m2_12 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==12) , 'salesVolume'].values
m2_11 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==11) , 'salesVolume'].values
m2_10 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==10) , 'salesVolume'].values
m2_09 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==9 ) , 'salesVolume'].values
m2_08 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==8 ) , 'salesVolume'].values
m2_07 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==7 ) , 'salesVolume'].values
m2_01_volum = evaluation_public.loc[(evaluation_public.regYear==2018)&(evaluation_public.regMonth==1 ), 'forecastVolum'].values * m2_01
m2_12_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==12), 'salesVolume'].values * m2_12
m2_11_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==11), 'salesVolume'].values * m2_11
m2_10_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==10), 'salesVolume'].values * m2_10
m2_09_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==9) , 'salesVolume'].values * m2_09
m2_08_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==8) , 'salesVolume'].values * m2_08
m2_07_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==7) , 'salesVolume'].values * m2_07
evaluation_public.loc[evaluation_public.regMonth==2, 'forecastVolum'] = m2_01_volum/2 m2_12_volum/4 m2_11_volum/8
m2_10_volum/16 m2_09_volum/32 m2_08_volum/64 m2_07_volum/64
##### 3月
m3_02 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2 ) , 'salesVolume'].values
m3_01 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1 ) , 'salesVolume'].values
m3_12 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==12) , 'salesVolume'].values
m3_11 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==11) , 'salesVolume'].values
m3_10 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==10) , 'salesVolume'].values
m3_09 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==9 ) , 'salesVolume'].values
m3_08 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==8 ) , 'salesVolume'].values
m3_02_volum = evaluation_public.loc[(evaluation_public.regYear==2018)&(evaluation_public.regMonth==2 ), 'forecastVolum'].values * m3_02
m3_01_volum = evaluation_public.loc[(evaluation_public.regYear==2018)&(evaluation_public.regMonth==1 ), 'forecastVolum'].values * m3_01
m3_12_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==12), 'salesVolume'].values * m3_12
m3_11_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==11), 'salesVolume'].values * m3_11
m3_10_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==10), 'salesVolume'].values * m3_10
m3_09_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==9) , 'salesVolume'].values * m3_09
m3_08_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==8) , 'salesVolume'].values * m3_08
evaluation_public.loc[evaluation_public.regMonth==3, 'forecastVolum'] = m3_02_volum/2 m3_01_volum/4 m3_12_volum/8
m3_11_volum/16 m3_10_volum/32 m3_09_volum/64 m3_08_volum/64
##### 4月
m4_03 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==4) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==3 ) , 'salesVolume'].values
m4_02 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==4) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==2 ) , 'salesVolume'].values
m4_01 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==4) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==1 ) , 'salesVolume'].values
m4_12 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==4) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==12) , 'salesVolume'].values
m4_11 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==4) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==11) , 'salesVolume'].values
m4_10 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==4) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==10) , 'salesVolume'].values
m4_09 = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==4) , 'salesVolume'].values / train_sales.loc[(train_sales.regYear==2016)&(train_sales.regMonth==9 ) , 'salesVolume'].values
m4_03_volum = evaluation_public.loc[(evaluation_public.regYear==2018)&(evaluation_public.regMonth==3 ), 'forecastVolum'].values * m4_03
m4_02_volum = evaluation_public.loc[(evaluation_public.regYear==2018)&(evaluation_public.regMonth==2 ), 'forecastVolum'].values * m4_02
m4_01_volum = evaluation_public.loc[(evaluation_public.regYear==2018)&(evaluation_public.regMonth==1 ), 'forecastVolum'].values * m4_01
m4_12_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==12), 'salesVolume'].values * m4_12
m4_11_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==11), 'salesVolume'].values * m4_11
m4_10_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==10), 'salesVolume'].values * m4_10
m4_09_volum = train_sales.loc[(train_sales.regYear==2017)&(train_sales.regMonth==9 ), 'salesVolume'].values * m4_09
evaluation_public.loc[evaluation_public.regMonth==4, 'forecastVolum'] = m4_03_volum/2 m4_02_volum/4 m4_01_volum/8
m4_12_volum/16 m4_11_volum/32 m3_10_volum/64 m4_09_volum/64
evaluation_public[['id','forecastVolum']].round().astype(int).to_csv('ccf_car_sales.csv', index=False)
