#将LBL1特征的值衍生为one-hot形式的新特征

piao=df_train_log.LBL1.value_counts().index

#先构造一个临时的df

df_tmp=pd.DataFrame({'USRID':df_train_log.drop_duplicates('USRID').USRID.values})

#将所有的新特征列都置为0

for i in piao:

    df_tmp['PIAO_'+i]=0

#进行分组便利，有这个特征就置为1，原数据每个USRID有多条记录，所以分组统计

group=df_train_log.groupby(['USRID'])

for k in group.groups.keys():

    t = group.get_group(k)

    id=t.USRID.value_counts().index[0]

    tmp_list=t.LBL1.value_counts().index

    for j in tmp_list:

        df_tmp['PIAO_'+j].loc[df_tmp.USRID==id]=1

group=df_train_log.groupby(['USRID'])

lt=[]

list_max_lbl1=[]

list_max_lbl2=[]

list_max_lbl3=[]

for k in group.groups.keys():

    t = group.get_group(k)
    #通过value_counts找出出现次数最多的项

    argmx = np.argmax(t['EVT_LBL'].value_counts())

    lbl1_max=np.argmax(t['LBL1'].value_counts())

    lbl2_max=np.argmax(t['LBL2'].value_counts())

    lbl3_max=np.argmax(t['LBL3'].value_counts())

    list_max_lbl1.append(lbl1_max)

    list_max_lbl2.append(lbl2_max)

    list_max_lbl3.append(lbl3_max)
    #只留下出现次数最多的项

    c = t[t['EVT_LBL']==argmx].drop_duplicates('EVT_LBL')
    #放入list中

    lt.append(c)
#构造一个新的df

df_train_log_new = pd.concat(lt)
#另外又构造了三个特征，LBL1-LBL3分别出现次数最多的项

df_train_log_new['LBL1_MAX']=list_max_lbl1

df_train_log_new['LBL2_MAX']=list_max_lbl2

df_train_log_new['LBL3_MAX']=list_max_lbl3

#创造临时df，星期三，星期六，星期七，都默认置为0

df_day=pd.DataFrame({'USRID':df_train_log.drop_duplicates('USRID').USRID.values})

df_day['weekday_3']=0

df_day['weekday_6']=0

df_day['weekday_7']=0

#分组统计，有就置为1，没有置为0

group=df_train_log.groupby(['USRID'])

for k in group.groups.keys():

    t = group.get_group(k)

    id=t.USRID.value_counts().index[0]

    tmp_list=t.occ_dayofweek.value_counts().index

    for j in tmp_list:

        if j==3:

            df_day['weekday_3'].loc[df_tmp.USRID==id]=1

        elif j==6:

            df_day['weekday_6'].loc[df_tmp.USRID==id]=1

        elif j==7:

            df_day['weekday_7'].loc[df_tmp.USRID==id]=1

#首先将日期转化为时间戳，并赋予一个新特征

tmp_list=[]

for i in df_train_log.OCC_TIM:

    d=datetime.datetime.strptime(str(i),"%Y-%m-%d %H:%M:%S")

    evt_time = time.mktime(d.timetuple())

    tmp_list.append(evt_time)

df_train_log['time']=tmp_list

#每下一行减去上一行，得到app停留时间

df_train_log['diff_time']=df_train_log.time-df_train_log.time.shift(1)

#构造一个新的dataFrame，分组得到查看app的天数

df_time=pd.DataFrame({'USRID':df_train_log.drop_duplicates('USRID').USRID.values})

#有几天查看

df_time['days']=0

group=df_train_log.groupby(['USRID'])

for k in group.groups.keys():

    t = group.get_group(k)

    id=set(t.USRID).pop()

    df_time['days'].loc[df_time.USRID==id]= len(t.occ_day.value_counts().index)

#去掉一些异常时间戳，比如间隔两天的相减，肯定不合适，na的也去掉了

df_train_log=df_train_log[(df_train_log.diff_time>0)&(df_train_log.diff_time<8000)]

#累计停留时间

group_stayTime=df_train_log['diff_time'].groupby(df_train_log['USRID']).sum()

#创造新的df

df_tmp=pd.DataFrame({'USRID':list(group_stayTime.index.values),'stay_time':list(group_stayTime.values)})

#合并成一个新的df

df=pd.merge(df_time,df_tmp,on=['USRID'],how='left')
#合并后，缺失的停留时间，置为0
df.fillna(0,axis=1,inplace=True)