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ML
ffm-baseline
Commits
91c4f980
Commit
91c4f980
authored
Jun 03, 2019
by
张彦钊
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修改esmm写入数据库时nearby的日记日记队列数量
parent
c9703d99
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2 changed files
with
4 additions
and
8 deletions
+4
-8
to_database.py
eda/esmm/Model_pipline/to_database.py
+4
-8
train.py
eda/esmm/Model_pipline/train.py
+0
-0
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eda/esmm/Model_pipline/to_database.py
View file @
91c4f980
...
...
@@ -19,12 +19,7 @@ def con_sql(sql):
return
result
def
nearby_set_join
(
lst
):
# return ','.join([str(i) for i in list(lst)])
return
','
.
join
([
str
(
i
)
for
i
in
lst
.
unique
()
.
tolist
()])
def
native_set_join
(
lst
):
def
set_join
(
lst
):
l
=
lst
.
unique
()
.
tolist
()
r
=
[
str
(
i
)
for
i
in
l
]
r
=
r
[:
500
]
...
...
@@ -40,7 +35,7 @@ def main():
df1
=
pd
.
read_csv
(
path
+
"/native/pred.txt"
,
sep
=
'
\t
'
,
header
=
None
,
names
=
[
"ctr"
,
"cvr"
,
"ctcvr"
])
df2
[
"ctr"
],
df2
[
"cvr"
],
df2
[
"ctcvr"
]
=
df1
[
"ctr"
],
df1
[
"cvr"
],
df1
[
"ctcvr"
]
df3
=
df2
.
groupby
(
by
=
[
"uid"
,
"city"
])
.
apply
(
lambda
x
:
x
.
sort_values
(
by
=
"ctcvr"
,
ascending
=
False
))
\
.
reset_index
(
drop
=
True
)
.
groupby
(
by
=
[
"uid"
,
"city"
])
.
agg
({
'cid_id'
:
native_
set_join
})
.
reset_index
(
drop
=
False
)
.
reset_index
(
drop
=
True
)
.
groupby
(
by
=
[
"uid"
,
"city"
])
.
agg
({
'cid_id'
:
set_join
})
.
reset_index
(
drop
=
False
)
df3
.
columns
=
[
"device_id"
,
"city_id"
,
"native_queue"
]
print
(
"native_device_count"
,
df3
.
shape
)
...
...
@@ -51,7 +46,8 @@ def main():
df1
=
pd
.
read_csv
(
path
+
"/nearby/pred.txt"
,
sep
=
'
\t
'
,
header
=
None
,
names
=
[
"ctr"
,
"cvr"
,
"ctcvr"
])
df2
[
"ctr"
],
df2
[
"cvr"
],
df2
[
"ctcvr"
]
=
df1
[
"ctr"
],
df1
[
"cvr"
],
df1
[
"ctcvr"
]
df4
=
df2
.
groupby
(
by
=
[
"uid"
,
"city"
])
.
apply
(
lambda
x
:
x
.
sort_values
(
by
=
"ctcvr"
,
ascending
=
False
))
.
reset_index
(
drop
=
True
)
.
groupby
(
by
=
[
"uid"
,
"city"
])
.
agg
({
'cid_id'
:
nearby_set_join
})
.
reset_index
(
drop
=
False
)
df4
=
df2
.
groupby
(
by
=
[
"uid"
,
"city"
])
.
apply
(
lambda
x
:
x
.
sort_values
(
by
=
"ctcvr"
,
ascending
=
False
))
\
.
reset_index
(
drop
=
True
)
.
groupby
(
by
=
[
"uid"
,
"city"
])
.
agg
({
'cid_id'
:
set_join
})
.
reset_index
(
drop
=
False
)
df4
.
columns
=
[
"device_id"
,
"city_id"
,
"nearby_queue"
]
print
(
"nearby_device_count"
,
df4
.
shape
)
...
...
eda/esmm/Model_pipline/train.py
View file @
91c4f980
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