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import os
from pyspark import SparkConf
from pyspark.sql import SparkSession
import sys
import time
from datetime import date, timedelta
import pandas as pd
from gensim.models import Word2Vec
import pickle
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
import utils.connUtils as connUtils
VERSION = "v1"
def getClickSql(start, end):
sql = """
SELECT DISTINCT t1.partition_date, t1.cl_id device_id, t1.card_id,t1.time_stamp,t1.page_stay
FROM
(
select partition_date,cl_id,business_id as card_id,time_stamp,page_stay
from online.bl_hdfs_maidian_updates
where action = 'page_view'
AND partition_date>='{startDay}' and partition_date<='{endDay}'
AND page_name='welfare_detail'
AND page_stay >= 10
AND cl_id is not null
AND cl_id != ''
AND business_id is not null
AND business_id != ''
group by partition_date,cl_id,business_id,time_stamp,page_stay
) AS t1
join
( --渠道，新老
SELECT distinct device_id
FROM online.ml_device_day_active_status
where partition_date>='{startDay}' and partition_date<='{endDay}'
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','','unknown')
AND first_channel_source_type not like 'promotion\_jf\_%'
) t2
on t1.cl_id = t2.device_id
LEFT JOIN
( --去除黑名单
select distinct device_id
from ML.ML_D_CT_DV_DEVICECLEAN_DIMEN_D
where PARTITION_DAY =regexp_replace(DATE_SUB(current_date,1) ,'-','')
AND is_abnormal_device = 'true'
)t3
on t3.device_id=t2.device_id
WHERE t3.device_id is null
""".format(startDay=start,endDay=end)
print(sql)
return sql
def addDays(n, format="%Y%m%d"):
return (date.today() + timedelta(days=n)).strftime(format)
def reverseCol(x):
res = []
datas = sorted(list(x.time_card))
last_time_stamp = int(datas[0].split("_")[0])
res_line = []
for d in datas:
time_stamp = int(d.split("_")[0])
cart_id = d.split("_")[1]
if (time_stamp - last_time_stamp) > 60 * 60:
if len(res_line) > 1:
res.append(res_line)
res_line = [cart_id]
else:
res_line.append(cart_id)
last_time_stamp = time_stamp
if len(res_line) > 1:
res.append(res_line)
return res
def get_spark(appName):
sparkConf = SparkConf()
sparkConf.set("spark.sql.crossJoin.enabled", True)
sparkConf.set("spark.debug.maxToStringFields", "100")
sparkConf.set("spark.tispark.plan.allow_index_double_read", False)
sparkConf.set("spark.tispark.plan.allow_index_read", True)
sparkConf.set("spark.hive.mapred.supports.subdirectories", True)
sparkConf.set("spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive", True)
sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
sparkConf.set("mapreduce.output.fileoutputformat.compress", False)
sparkConf.set("mapreduce.map.output.compress", False)
spark = (SparkSession
.builder
.config(conf=sparkConf)
.appName(appName)
.enableHiveSupport()
.getOrCreate())
return spark
if __name__ == '__main__':
start = time.time()
# 入参
trainDays = int(sys.argv[1])
spark = get_spark("service_embedding")
spark.sparkContext.setLogLevel("ERROR")
print('trainDays:{}'.format(trainDays), flush=True)
endDay = addDays(0)
startDay = addDays(-int(trainDays))
print("train_data start:{} end:{}".format(startDay, endDay))
# 行为数据
clickSql = getClickSql(startDay, endDay)
clickDF = spark.sql(clickSql)
df = clickDF.toPandas()
df = pd.DataFrame(df)
# pd.DataFrame(df).to_csv("/tmp/service_click.csv",index=False)
print("count",df.count())
df["time_card"] = df["time_stamp"].map(str) + "_" + df["card_id"].map(str)
new_df = df.groupby(["device_id"]).apply(reverseCol).to_frame("card_seq").reset_index()
df1 = new_df.loc[new_df["card_seq"].map(len) > 1]
print("user seq size:",df1.count())
datas = df1["card_seq"].tolist()
train_datas = []
for d in datas:
train_datas.extend(d)
print("train size:",len(train_datas))
model = Word2Vec(train_datas, sg=1, vector_size=16, window=5, epochs=50)
s = pickle.dumps(model)
# 模型保存
conn = connUtils.getRedisConn()
model_key = "strategy:word2vec:{}:{}".format("service",VERSION)
model_status_key = "strategy:word2vec:status:{}:{}".format("service",VERSION)
conn.set(model_key,s)
conn.expire(model_key,60*60*24*30)
# 模型更新状态保存
conn.set(model_status_key,"1")
conn.expire(model_status_key,60*60*24*30)
conn.close()