Merge branch 'master' of git.wanmeizhensuo.com:ML/ffm-baseline

pretty output

diaryCandidateSet.py

@@ -35,7 +35,6 @@ def get_cityList():
     cityList = con_sql(sql)
     cityList.to_csv(DIRECTORY_PATH + "diaryTestSet/cityList.csv",index=False)
     cityList = cityList[0].values.tolist()
-    cityList.remove('worldwide')
     print("成功获取全国城市列表")
     return cityList
 

predictDiary.py

@@ -25,8 +25,8 @@ def feature_en(user_profile):
     # 虽然预测y，但ffm转化需要y，并不影响预测结果
     data["y"] = 0
     data = data.drop("city_id", axis=1)
-    print(data.head(2))
-
+    print(data.head(1))
+    print("特征工程处理结束")
     return data
 
 
@@ -37,7 +37,7 @@ def transform_ffm_format(df, device_id):
         ffm_format_pandas = pickle.load(f)
         data = ffm_format_pandas.transform(df)
         now = datetime.now().strftime("%Y-%m-%d-%H-%M")
-
+        print("ffm格式转化结束")
         predict_file_name = DIRECTORY_PATH + "result/{0}_{1}DiaryTop2000.csv".format(device_id, now)
         data.to_csv(predict_file_name, index=False,header=None)
         return predict_file_name

userProfile.py

@@ -27,10 +27,10 @@ def fetch_user_profile(device_id):
         if user_profile.empty:
             print("没有获取到该用户对应的city_id")
             return {}, user_profile.empty
-
-        user_profile = user_profile.rename(columns={0:"device_id",1:"city_id"})
-        print("成功获取该用户对应的city_id")
-        user_profile_dict = {}
-        for i in user_profile.columns:
-            user_profile_dict[i] = user_profile.loc[0, i]
-        return user_profile_dict, user_profile.empty
+        else:
+            user_profile = user_profile.rename(columns={0:"device_id",1:"city_id"})
+            print("成功获取该用户对应的city_id")
+            user_profile_dict = {}
+            for i in user_profile.columns:
+                user_profile_dict[i] = user_profile.loc[0, i]
+            return user_profile_dict, user_profile.empty