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strategy_embedding
Commits
c61d32b0
Commit
c61d32b0
authored
Dec 22, 2020
by
赵威
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get answer click ids
parent
27e9e630
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answer.py
word_vector/answer.py
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word_vector/answer.py
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c61d32b0
import
multiprocessing
import
os
import
sys
import
time
from
collections
import
defaultdict
sys
.
path
.
append
(
os
.
path
.
realpath
(
"."
))
from
gensim.models
import
Word2Vec
,
word2vec
from
utils.date
import
get_ndays_before_no_minus
,
get_ndays_before_with_format
from
utils.files
import
DATA_PATH
,
MODEL_PATH
from
utils.spark
import
get_spark
answer_click_ids_model_path
=
os
.
path
.
join
(
MODEL_PATH
,
"answer_click_ids_item2vec_model"
)
def
get_answer_click_data
(
spark
,
start
,
end
):
reg
=
r"""^\\d+$"""
sql
=
"""
SELECT DISTINCT t1.partition_date, t1.cl_id, cast(t1.business_id as int) card_id, t1.app_session_id
FROM
(select partition_date,cl_id,business_id,action,page_name,page_stay, app_session_id
from online.bl_hdfs_maidian_updates
where action = 'page_view'
AND partition_date BETWEEN '{}' AND '{}'
AND page_name='answer_detail'
AND page_stay>=1
AND cl_id is not null
AND cl_id != ''
AND business_id is not null
AND business_id != ''
AND business_id rlike '{}'
) AS t1
JOIN
(select partition_date,active_type,first_channel_source_type,device_id
from online.ml_device_day_active_status
where partition_date BETWEEN '{}' AND '{}'
AND active_type IN ('1', '2', '4')
AND first_channel_source_type not IN ('yqxiu1','yqxiu2','yqxiu3','yqxiu4','yqxiu5','mxyc1','mxyc2','mxyc3'
,'wanpu','jinshan','jx','maimai','zhuoyi','huatian','suopingjingling','mocha','mizhe','meika','lamabang'
,'js-az1','js-az2','js-az3','js-az4','js-az5','jfq-az1','jfq-az2','jfq-az3','jfq-az4','jfq-az5','toufang1'
,'toufang2','toufang3','toufang4','toufang5','toufang6','TF-toufang1','TF-toufang2','TF-toufang3','TF-toufang4'
,'TF-toufang5','tf-toufang1','tf-toufang2','tf-toufang3','tf-toufang4','tf-toufang5','benzhan','promotion_aso100'
,'promotion_qianka','promotion_xiaoyu','promotion_dianru','promotion_malioaso','promotion_malioaso-shequ'
,'promotion_shike','promotion_julang_jl03','promotion_zuimei')
AND first_channel_source_type not LIKE 'promotion
\\
_jf
\\
_
%
') as t2
ON t1.cl_id = t2.device_id
AND t1.partition_date = t2.partition_date
LEFT JOIN
(
SELECT DISTINCT device_id
FROM ml.ml_d_ct_dv_devicespam_d --去除机构刷单设备,即作弊设备(浏览和曝光事件去除)
WHERE partition_day='{}'
UNION ALL
SELECT DISTINCT device_id
FROM dim.dim_device_user_staff --去除内网用户
)spam_pv
on spam_pv.device_id=t1.cl_id
LEFT JOIN
(
SELECT partition_date,device_id
FROM
(--找出user_id当天活跃的第一个设备id
SELECT user_id,partition_date,
if(size(device_list) > 0, device_list [ 0 ], '') AS device_id
FROM online.ml_user_updates
WHERE partition_date>='{}' AND partition_date<'{}'
)t1
JOIN
( --医生账号
SELECT distinct user_id
FROM online.tl_hdfs_doctor_view
WHERE partition_date = '{}'
--马甲账号/模特用户
UNION ALL
SELECT user_id
FROM ml.ml_c_ct_ui_user_dimen_d
WHERE partition_day = '{}'
AND (is_puppet = 'true' or is_classifyuser = 'true')
UNION ALL
--公司内网覆盖用户
select distinct user_id
from dim.dim_device_user_staff
UNION ALL
--登陆过医生设备
SELECT distinct t1.user_id
FROM
(
SELECT user_id, v.device_id as device_id
FROM online.ml_user_history_detail
LATERAL VIEW EXPLODE(device_history_list) v AS device_id
WHERE partition_date = '{}'
)t1
JOIN
(
SELECT device_id
FROM online.ml_device_history_detail
WHERE partition_date = '{}'
AND is_login_doctor = '1'
)t2
ON t1.device_id = t2.device_id
)t2
on t1.user_id=t2.user_id
group by partition_date,device_id
)dev
on t1.partition_date=dev.partition_date and t1.cl_id=dev.device_id
WHERE (spam_pv.device_id IS NULL or spam_pv.device_id ='')
and (dev.device_id is null or dev.device_id ='')
"""
.
format
(
start
,
end
,
reg
,
start
,
end
,
end
,
start
,
end
,
end
,
end
,
end
,
end
)
# print("sql", flush=True)
# print(sql, flush=True)
df
=
spark
.
sql
(
sql
)
return
df
if
__name__
==
"__main__"
:
begin_time
=
time
.
time
()
spark
=
get_spark
(
"answer_click_ids"
)
click_df
=
get_answer_click_data
(
spark
,
get_ndays_before_no_minus
(
180
),
get_ndays_before_no_minus
(
1
))
click_df
.
show
(
5
,
False
)
print
(
click_df
.
count
())
# spark-submit --master yarn --deploy-mode client --queue root.strategy --driver-memory 16g --executor-memory 1g --executor-cores 1 --num-executors 70 --conf spark.default.parallelism=100 --conf spark.storage.memoryFraction=0.5 --conf spark.shuffle.memoryFraction=0.3 --conf spark.locality.wait=0 --jars /srv/apps/tispark-core-2.1-SNAPSHOT-jar-with-dependencies.jar,/srv/apps/spark-connector_2.11-1.9.0-rc2.jar,/srv/apps/mysql-connector-java-5.1.38.jar /srv/apps/strategy_embedding/word_vector/answer.py
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