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ML
ffm-baseline
Commits
ff8ced2c
Commit
ff8ced2c
authored
May 21, 2019
by
张彦钊
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修改测试文件
parent
9d66cd16
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multi.py
tensnsorflow/multi.py
+6
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tensnsorflow/multi.py
View file @
ff8ced2c
...
@@ -105,8 +105,8 @@ def feature_engineer():
...
@@ -105,8 +105,8 @@ def feature_engineer():
print
(
"va write done"
)
print
(
"va write done"
)
spark
.
createDataFrame
(
train
)
.
toDF
(
"app_list"
,
"level2_ids"
,
"level3_ids"
,
"stat_date"
,
"ucity_id"
,
"ccity_name"
,
"device_type"
,
"manufacturer"
,
spark
.
createDataFrame
(
train
)
.
toDF
(
"app_list"
,
"level2_ids"
,
"level3_ids"
,
"stat_date"
,
"ucity_id"
,
"ccity_name"
,
"device_type"
,
"manufacturer"
,
"channel"
,
"top"
,
"time"
,
"hospital_id"
,
"treatment_method"
,
"price_min"
,
"channel"
,
"top"
,
"time"
,
"hospital_id"
,
"treatment_method"
,
"price_min"
,
"price_max"
,
"treatment_time"
,
"maintain_time"
,
"recover_time"
,
"y"
,
"z"
)
\
"price_max"
,
"treatment_time"
,
"maintain_time"
,
"recover_time"
,
"y"
,
"z"
)
\
.
write
.
format
(
"tfrecords"
)
.
option
(
"recordType"
,
"Example"
)
.
save
(
path
=
path
+
"tr/"
,
mode
=
"overwrite"
)
.
repartition
(
1
)
.
write
.
format
(
"tfrecords"
)
.
option
(
"recordType"
,
"Example"
)
.
save
(
path
=
path
+
"tr/"
,
mode
=
"overwrite"
)
print
(
"done"
)
print
(
"done"
)
rdd
.
unpersist
()
rdd
.
unpersist
()
...
@@ -165,8 +165,8 @@ def get_predict(date,value_map,app_list_map,level2_map,level3_map):
...
@@ -165,8 +165,8 @@ def get_predict(date,value_map,app_list_map,level2_map,level3_map):
.
toDF
(
"app_list"
,
"level2_ids"
,
"level3_ids"
,
"y"
,
"z"
,
"ucity_id"
,
.
toDF
(
"app_list"
,
"level2_ids"
,
"level3_ids"
,
"y"
,
"z"
,
"ucity_id"
,
"ccity_name"
,
"device_type"
,
"manufacturer"
,
"channel"
,
"time"
,
"hospital_id"
,
"ccity_name"
,
"device_type"
,
"manufacturer"
,
"channel"
,
"time"
,
"hospital_id"
,
"treatment_method"
,
"price_min"
,
"price_max"
,
"treatment_time"
,
"maintain_time"
,
"treatment_method"
,
"price_min"
,
"price_max"
,
"treatment_time"
,
"maintain_time"
,
"recover_time"
,
"top"
,
"stat_date"
)
.
write
.
format
(
"tfrecords"
)
.
option
(
"recordType"
,
"Example"
)
\
"recover_time"
,
"top"
,
"stat_date"
)
.
write
.
format
(
"tfrecords"
)
.
option
(
"recordType"
,
"Example"
)
\
.
save
(
path
=
path
+
"native/"
,
mode
=
"overwrite"
)
.
repartition
(
1
)
.
save
(
path
=
path
+
"native/"
,
mode
=
"overwrite"
)
nearby_pre
=
spark
.
createDataFrame
(
rdd
.
filter
(
lambda
x
:
x
[
6
]
==
1
)
.
map
(
lambda
x
:
(
x
[
3
],
x
[
4
],
x
[
5
])))
\
nearby_pre
=
spark
.
createDataFrame
(
rdd
.
filter
(
lambda
x
:
x
[
6
]
==
1
)
.
map
(
lambda
x
:
(
x
[
3
],
x
[
4
],
x
[
5
])))
\
.
toDF
(
"city"
,
"uid"
,
"cid_id"
)
.
toDF
(
"city"
,
"uid"
,
"cid_id"
)
...
@@ -181,8 +181,8 @@ def get_predict(date,value_map,app_list_map,level2_map,level3_map):
...
@@ -181,8 +181,8 @@ def get_predict(date,value_map,app_list_map,level2_map,level3_map):
.
toDF
(
"app_list"
,
"level2_ids"
,
"level3_ids"
,
"y"
,
"z"
,
"ucity_id"
,
.
toDF
(
"app_list"
,
"level2_ids"
,
"level3_ids"
,
"y"
,
"z"
,
"ucity_id"
,
"ccity_name"
,
"device_type"
,
"manufacturer"
,
"channel"
,
"time"
,
"hospital_id"
,
"ccity_name"
,
"device_type"
,
"manufacturer"
,
"channel"
,
"time"
,
"hospital_id"
,
"treatment_method"
,
"price_min"
,
"price_max"
,
"treatment_time"
,
"maintain_time"
,
"treatment_method"
,
"price_min"
,
"price_max"
,
"treatment_time"
,
"maintain_time"
,
"recover_time"
,
"top"
,
"stat_date"
)
.
write
.
format
(
"tfrecords"
)
.
option
(
"recordType"
,
"Example"
)
\
"recover_time"
,
"top"
,
"stat_date"
)
.
write
.
format
(
"tfrecords"
)
.
option
(
"recordType"
,
"Example"
)
\
.
save
(
path
=
path
+
"nearby/"
,
mode
=
"overwrite"
)
.
repartition
(
1
)
.
save
(
path
=
path
+
"nearby/"
,
mode
=
"overwrite"
)
rdd
.
unpersist
()
rdd
.
unpersist
()
...
...
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