# -*- coding: utf-8 -*-

"""

Created on Sat Oct 28 16:16:39 2017

@author: 麦芽的香气

"""

import pandas as pd

import numpy as np

from sklearn import preprocessing

import xgboost as xgb

import lgbtraining as lgb

df=pd.read_csv('ccf_first_round_user_shop_behavior.csv')#读入训练数据一

shop=pd.read_csv('ccf_first_round_shop_info.csv')#读入训练数据二

test=pd.read_csv(u'AB榜测试集-evaluation_public.csv')#读入测试数据

df=pd.merge(df,shop[['shop_id','mall_id']],how='left',on='shop_id') #合并训练数据二的mall_id到训练数据一

df['time_stamp']=pd.to_datetime(df['time_stamp']) #将字符串转化为时间格式

train=pd.concat([df,test])#将训练数据与测试数据上下合并

mall_list=list(set(list(shop.mall_id))) #罗列出所有商场

result=pd.DataFrame() #定义输出数据框

j=0

for mall in mall_list: #循环每个商场

train1=train[train.mall_id==mall].reset_index(drop=True) #选出该商场的所有训练测试数据

l=[]

wifi_dict = {} #列出所有wifi，形成一个字典

for index,row in train1.iterrows(): #迭代该商场的所有训练测试数据的每一行

r = {}

wifi_list = [wifi.split('|') for wifi in row['wifi_infos'].split(';')] #分割每行数据中出现的wifi

for i in wifi_list: #对wifi出现的次数计数，并存入wifi_dict字典

r[i[0]]=int(i[1])

if i[0] not in wifi_dict:

wifi_dict[i[0]]=1

else:

wifi_dict[i[0]]+=1

l.append(r)

delate_wifi=[]

for i in wifi_dict: #删除所有出现次数少于5次的wifi

if wifi_dict[i]<15:

delate_wifi.append(i)

m=[]

for row in l: #删除wifi之后的训练测试数据

new={}

for n in row.keys():

if n not in delate_wifi:

new[n]=row[n]

m.append(new)

train1 = pd.concat([train1,pd.DataFrame(m)], axis=1) #左右合并原始训练测试数据与新生成训练测试数据

df_train=train1[train1.shop_id.notnull()] #根据有shop_id，分割数据为训练数据，

df_test=train1[train1.shop_id.isnull()] #根据无shop_id，分割数据为测试数据，

lbl = preprocessing.LabelEncoder() #对标签进行编码为1—N

lbl.fit(list(df_train['shop_id'].values))

df_train['label'] = lbl.transform(list(df_train['shop_id'].values))

num_class=df_train['label'].max()+1

# 训练函数参数设定

params = {

'objective': 'multi:softmax',

'eta': 0.1,

'max_depth': 9,

'eval_metric': 'merror',

'seed': 0,

'missing': -999,

'num_class':num_class,

'silent' : 1

}

#从数据中去掉多余的列

feature=[x for x in train1.columns if x not in ['user_id','label','shop_id','time_stamp','mall_id','wifi_infos']]

df_train_1 = df_train[feature] #训练数据对null值进行填充-100

df_train_1 = df_train_1.where(df_train_1.notnull(), -100)

xgbtrain = xgb.DMatrix(df_train_1, df_train['label'])

df_test_1 = df_test[feature] #测试数据对null值进行填充-100

df_test_1 = df_test_1.where(df_test_1.notnull(), -100)

xgbtest = xgb.DMatrix(df_test_1)

watchlist = [ (xgbtrain,'train'), (xgbtrain, 'test') ]

num_rounds=100

model = xgb.train(params, xgbtrain, num_rounds, watchlist, early_stopping_rounds=15) #训练模型

df_test['label']=model.predict(xgbtest) #预测

df_test['shop_id']=df_test['label'].apply(lambda x:lbl.inverse_transform(int(x))) #对标签1-N转化为shop_id

r=df_test[['row_id','shop_id']] #选出r

result=pd.concat([result,r])

j = j + 1

print j

result['row_id']=result['row_id'].astype('int')

result.to_csv('sub.csv',index=False)