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ML
ffm-baseline
Commits
24c102d5
Commit
24c102d5
authored
Dec 13, 2018
by
王志伟
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Merge branch 'master' of
http://git.wanmeizhensuo.com/ML/ffm-baseline
parents
144cb1ae
461a6b9e
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26 additions
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+26
-6
ffm.py
tensnsorflow/ffm.py
+26
-6
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tensnsorflow/ffm.py
View file @
24c102d5
...
...
@@ -192,6 +192,24 @@ def transform(a,validate_date):
return
model
def
get_user_feature
():
db
=
pymysql
.
connect
(
host
=
'10.66.157.22'
,
port
=
4000
,
user
=
'root'
,
passwd
=
'3SYz54LS9#^9sBvC'
,
db
=
'jerry_test'
)
sql
=
"select max(stat_date) from esmm_train_data"
validate_date
=
con_sql
(
db
,
sql
)[
0
]
.
values
.
tolist
()[
0
]
print
(
"validate_date:"
+
validate_date
)
temp
=
datetime
.
datetime
.
strptime
(
validate_date
,
"
%
Y-
%
m-
%
d"
)
start
=
(
temp
-
datetime
.
timedelta
(
days
=
2
))
.
strftime
(
"
%
Y-
%
m-
%
d"
)
db
=
pymysql
.
connect
(
host
=
'10.66.157.22'
,
port
=
4000
,
user
=
'root'
,
passwd
=
'3SYz54LS9#^9sBvC'
,
db
=
'jerry_test'
)
sql
=
"select e.device_id,e.y,e.z,e.stat_date,e.ucity_id,e.cid_id,e.clevel1_id,e.ccity_name,"
\
"u.device_type,u.manufacturer,u.channel,home.total"
\
"from (esmm_train_data e left join user_feature u on e.device_id = u.device_id) "
\
"left join home_tab_click home on e.device_id = home.device_id"
\
"where e.stat_date >= '{}'"
.
format
(
start
)
df
=
con_sql
(
db
,
sql
)
df
=
df
.
rename
(
columns
=
{
0
:
"device_id"
,
1
:
"y"
,
2
:
"z"
,
3
:
"stat_date"
,
4
:
"ucity_id"
,
5
:
"cid_id"
,
6
:
"clevel1_id"
,
7
:
"ccity_name"
})
print
(
df
.
head
(
2
))
def
get_statistics
():
db
=
pymysql
.
connect
(
host
=
'10.66.157.22'
,
port
=
4000
,
user
=
'root'
,
passwd
=
'3SYz54LS9#^9sBvC'
,
db
=
'jerry_test'
)
sql
=
"select device_id,total,精选,直播,鼻部,眼部,微整,牙齿,轮廓,美肤抗衰,"
\
...
...
@@ -258,13 +276,15 @@ def get_predict_set(ucity_id, cid,model):
if
__name__
==
"__main__"
:
get_user_feature
()
path
=
"/home/gmuser/ffm/"
a
=
time
.
time
()
df
,
validate_date
,
ucity_id
,
cid
=
get_data
()
model
=
transform
(
df
,
validate_date
)
get_predict_set
(
ucity_id
,
cid
,
model
)
b
=
time
.
time
()
print
(
"cost(分钟)"
)
print
((
b
-
a
)
/
60
)
#
df, validate_date, ucity_id, cid = get_data()
#
model = transform(df, validate_date)
#
get_predict_set(ucity_id, cid,model)
#
b = time.time()
#
print("cost(分钟)")
#
print((b-a)/60)
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