美购精排模型

shell/models_upload.sh

+service_modelPath = /srv/apps/tensorServing_models/service
+cd /srv/apps/tensorServing_models
+#文件打包
+tar -czvf model_service.tar.gz $service_modelPath
+#上传至coding
+curl -T model_service.tar.gz -u guoyu@igengmei.com:Gengmei123! "https://gengmei-generic.pkg.coding.net/tob/mode/service"

shell/service_train.sh

+service_modelPath = /srv/apps/tensorServing_models/service
+rm -rf $service_modelPath/*
+source /srv/envs/serviceRec/bin/activate
+python /srv/apps/serviceRec/train/train_service.py > /srv/apps/serviceRec/logs/train_service_log.log
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shell/service_train_upload.sh

-modelPath = /srv/apps/serviceRec/models/service
-
-source /srv/envs/serviceRec/bin/activate
-rm -rf $modelPath
-python /srv/apps/serviceRec/train/train_service.py > /srv/apps/serviceRec/logs/train_service_log.log
-
-#文件打包
-#tar -czvf model_service.tar.gz $modelPath
-#上传至coding
-#curl -T model_service.tar.gz -u guoyu@igengmei.com:Gengmei123! "https://gengmei-generic.pkg.coding.net/tob/mode/service"
-#logging "upload model_service.tar.gz success... "
-

spark/featureEng.py

@@ -22,6 +22,8 @@ import json
 
 sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
 import utils.connUtils as connUtils
+import utils.configUtils as configUtils
+
 
 
 
@@ -54,7 +56,7 @@ ITEM_NUMBER_COLUMNS = ["lowest_price","smart_rank2","case_count","ordered_user_i
 ITEM_CATE_COLUMNS = ["service_type","doctor_type","doctor_id","doctor_famous","hospital_id","hospital_city_tag_id","hospital_type","hospital_is_high_quality"]
 NUMBER_PRECISION = 2
 
-VERSION = "v1"
+VERSION = configUtils.SERVICE_VERSION
 FEATURE_USER_KEY = "Strategy:rec:feature:service:" + VERSION + ":user:"
 FEATURE_ITEM_KEY = "Strategy:rec:feature:service:" + VERSION + ":item:"
 FEATURE_VOCAB_KEY = "Strategy:rec:vocab:service:" + VERSION

train/train_service.py

@@ -7,6 +7,7 @@ import os
 
 sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
 import utils.connUtils as connUtils
+import utils.configUtils as configUtils
 
 ITEM_NUMBER_COLUMNS = ["item_"+c for c in ["smart_rank2"]]
 embedding_columns = ["itemid","userid"] + ["item_"+c for c in ["doctor_id","hospital_id"]]
@@ -18,11 +19,11 @@ one_hot_columns = ["item_"+c for c in ["service_type","doctor_type","doctor_famo
 # data_path_train = "/Users/zhigangzheng/Desktop/work/guoyu/service_sort/train/part-00000-a61205d1-ad4e-4fa7-895d-ad8db41189e6-c000.csv"
 # data_path_test = "/Users/zhigangzheng/Desktop/work/guoyu/service_sort/train/part-00000-a61205d1-ad4e-4fa7-895d-ad8db41189e6-c000.csv"
 
-version = "v1"
+VERSION = configUtils.SERVICE_VERSION
 trainDay = time.strftime("%Y%m%d", time.localtime())
-data_path_train = "/data/files/service_feature_{}_train.csv".format(version)
-data_path_test = "/data/files/service_feature_{}_test.csv".format(version)
-model_file = "/srv/apps/serviceRec/models/service/" + trainDay
+data_path_train = "/data/files/service_feature_{}_train.csv".format(VERSION)
+data_path_test = "/data/files/service_feature_{}_test.csv".format(VERSION)
+model_file = configUtils.SERVICE_MODEL_PATH + "/" + trainDay
 
 def is_float(s):
     try:
@@ -199,7 +200,7 @@ if __name__ == '__main__':
 
     # redis中加载数据字典
     print("redis 中加载模型字典...")
-    data_vocab = getDataVocabFromRedis(version)
+    data_vocab = getDataVocabFromRedis(VERSION)
     assert data_vocab
 
     print("读取数据...")

utils/configUtils.py

+SERVICE_VERSION="v1"
+SERVICE_MODEL_PATH = "/srv/apps/tensorServing_models/service"
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