创建conda环境时候，environment.yaml 添加pip的国内源文件

environment.yaml

@@ -10,3 +10,5 @@ dependencies:
   - pip
   - pip:
     - -r requirements.txt
+    - -i http://pypi.mirrors.ustc.edu.cn/simple/
+    - --trusted-host pypi.mirrors.ustc.edu.cn

src/core/recommender.py

@@ -23,7 +23,7 @@ from ydl_ai_recommender.src.core.profile import encode_user_profile
 from ydl_ai_recommender.src.utils import get_conf_path, get_data_path, read_user_encoder_dict, read_counselors, get_project_path
 from ydl_ai_recommender.src.utils.log import create_logger
 
-logger = create_logger(__name__, 'service_xgb.log', is_rotating=True)
+logger = create_logger(__name__, 'recommender.log', is_rotating=True)
 
 
 def cost_time(desc):
@@ -37,7 +37,7 @@ def cost_time(desc):
     return the_func_cost_time
 
 
-class Recommender():
+class Recommender:
 
     def __init__(self) -> None:
         self.logger = create_logger(__name__, 'recommender.log')
@@ -256,6 +256,7 @@ class ItemCFRecommender(Recommender):
             counselors = counselors[:size]
         return counselors
 
+
 class RecommendByXgboost(Recommender):
     def __init__(self, top_n=5, k=20, is_use_db=True, u2c='combination', c2c=None) -> None:
         super().__init__()
@@ -278,14 +279,12 @@ class RecommendByXgboost(Recommender):
         self.model = xgb.XGBClassifier(objective='binary:logistic', nthread=-1, **self.params)
         self.model.load_model(os.path.join(get_project_path(), 'model_data/xgb_model.bin'))
 
-    @cost_time(desc='')
+    @cost_time(desc='召回咨询师')
     def recall_data(self, user_id, size=0, is_merge=True):
         return self.recommender.recommend(user_id, size=size, is_merge=True)
 
     @cost_time(desc='模型推荐整个流程')
     def recommend(self, user_id, size=0, is_merge=True):
-
-        s_u_profile_time = time.time()
         user_profile = self.get_user_profile(user_id)
         if not user_profile:
             return self._recommend_top(size)
@@ -299,7 +298,7 @@ class RecommendByXgboost(Recommender):
 
         pre_time = time.time()
         predit_result = self.model.predict_proba(predit_data)[:, 1]
-        logger.info('predit_time:{}ms'.format(int((time.time()-pre_time)*1000)))
+        self.logger.info('predit_time:{}ms'.format(int((time.time()-pre_time)*1000)))
         result_dict = dict(zip(doctor_ids, predit_result))
         result_dict = sorted(result_dict.items(), key=lambda x:x[1], reverse=True)
         recommend_data = [{
@@ -390,8 +389,6 @@ class RecommendByXgboost(Recommender):
                 encoder_result.append(value_convert_dict.get(val, 0))
         if len(encoder_result)<5:
             encoder_result.extend([0]*(5-len(encoder_result)))
-
-
         return encoder_result
 
     def get_encoder_from_dict(self, feature_name, feature_value):
@@ -399,8 +396,6 @@ class RecommendByXgboost(Recommender):
         if value_convert_dict is None:
             return 0
         return value_convert_dict.get(str(feature_value), 0)
-    def aa(self):
-        pass
 
 
 if __name__ == '__main__':