liqian 2 năm trước cách đây
mục cha
commit
a3e3c8a364
2 tập tin đã thay đổi với 8 bổ sung6 xóa
  1. 6 4
      config.py
  2. 2 2
      user_group_update.py

+ 6 - 4
config.py

@@ -641,6 +641,7 @@ class BaseConfig(object):
         'data5': APP_TYPE['LAO_HAO_KAN_VIDEO'],  # 老好看视频
         'data6': APP_TYPE['ZUI_JING_QI'],  # 票圈最惊奇
     }
+
     # 广告模型用户分组类别
     AD_MID_GROUP = {
         'class1': [
@@ -662,11 +663,13 @@ class BaseConfig(object):
             'return0share2_nmids'
         ]
     }
+
     # 免广告用户组列表
     NO_AD_MID_GROUP_LIST = {
         'class1': ['return25_nmids'],
         'class2': ['return30_nmids'],
     }
+
     # 广告模型用户数据
     AD_USER_PARAMS = {
         'data_params': {
@@ -700,6 +703,7 @@ class BaseConfig(object):
             {'data': 'data1', 'rule': 'rule3'},
         ]
     }
+
     # 广告模型abtest配置
     AD_ABTEST_CONFIG = {
         # 票圈vlog
@@ -761,8 +765,6 @@ class BaseConfig(object):
     KEY_NAME_PREFIX_MID_GROUP = 'mid:group:'
     # 广告推荐阈值结果存放 redis key 前缀,完整格式:ad:threshold:{abtestId}:{abtestConfigTag}:{group}
     KEY_NAME_PREFIX_AD_THRESHOLD = 'ad:threshold:'
-    # 免广告用户组列表
-    NO_AD_MID_GROUP_LIST = ['return25_nmids']
 
 
 class DevelopmentConfig(BaseConfig):
@@ -1087,8 +1089,8 @@ class ProductionConfig(BaseConfig):
 
 def set_config():
     # 获取环境变量 ROV_OFFLINE_ENV
-    # env = os.environ.get('ROV_OFFLINE_ENV')
-    env = 'dev'
+    env = os.environ.get('ROV_OFFLINE_ENV')
+    # env = 'dev'
     if env is None:
         # log_.error('ENV ERROR: is None!')
         return

+ 2 - 2
user_group_update.py

@@ -46,10 +46,10 @@ def to_redis(group, mid_list, class_key_list):
 def update_user_group_to_redis(project, table, dt, app_type_list, features, ad_mid_group_key_params):
     """更新mid对应分组到redis中"""
     # 获取用户分组数据
-    feature_df = get_feature_data(project=project, table=table, features=features[:-2], dt=dt)
+    feature_df = get_feature_data(project=project, table=table, features=features, dt=dt)
     feature_df['apptype'] = feature_df['apptype'].astype(int)
     feature_df = feature_df[feature_df['apptype'].isin(app_type_list)]
-    print(len(feature_df))
+    # print(len(feature_df))
     # group_list = features[1:]
     pool = multiprocessing.Pool(processes=len(ad_mid_group_key_params))
     for group, class_key_list in ad_mid_group_key_params.items():