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

add a comment and new a class ClkCidUidRate

diaryCandidateSet.py

@@ -7,12 +7,8 @@ from config import *
 # 候选集cid只能从训练数据集cid中选择
 def filter_cid(df):
     data_set_cid = pd.read_csv(DIRECTORY_PATH + "data_set_cid.csv")["cid"].values.tolist()
-    print("过滤前样本大小:")
-    print(df.shape)
     if not df.empty:
         df = df.loc[df["cid"].isin(data_set_cid)]
-        print("过滤后样本大小:")
-        print(df.shape)
     return df
 
 

predictDiary.py

@@ -94,17 +94,19 @@ def router(device_id):
 
 if __name__ == "__main__":
     # TODO 如果耗时小于一分钟，下一次取到的device_id和上一次相同
-
     while True:
         start = time.time()
         empty,device_id_list = get_active_users()
         if empty:
             time.sleep(10)
         else:
+            old_device_id_list = pd.read_csv(DIRECTORY_PATH + "data_set_device_id.csv")["device_id"].values.tolist()
             for device_id in device_id_list:
+                if device_id in old_device_id_list:
                     router(device_id)
-
+                else:
+                    print("该用户不是老用户，不能预测")
             end = time.time()
             time_cost = (end - start)
-            print("预测耗时{}秒".format(time_cost))
+            print("耗时{}秒".format(time_cost))
 

processData.py

@@ -60,6 +60,13 @@ def feature_en():
     print(cid_df.head(2))
     cid_df.to_csv(DIRECTORY_PATH + "data_set_cid.csv", index=False)
 
+    # 将device_id 保存。目的是为了判断预测的device_id是否在这个集合里,如果不在，不需要预测
+    data_set_device_id = data["device_id"].unique()
+    device_id_df = pd.DataFrame()
+    device_id_df['device_id'] = data_set_device_id
+    print("data_set_device_id :")
+    print(device_id_df.head(2))
+    device_id_df.to_csv(DIRECTORY_PATH + "data_set_device_id.csv", index=False)
     return data, test_number, validation_number
 
 
@@ -99,8 +106,5 @@ def ffm_transform(data, test_number, validation_number):
     train.to_csv(DIRECTORY_PATH + "train{0}-{1}.csv".format(DATA_START_DATE, VALIDATION_DATE), index=False, header=None)
 
 
-if __name__ == "__main__":
-    data_fe = feature_en()
-    ffm_transform(data_fe)
 
 

train.py

@@ -5,9 +5,10 @@ from diaryCandidateSet import get_eachCityDiaryTop3000
 
 # 把数据获取、特征转换、模型训练的模型串联在一起
 if __name__ == "__main__":
-    data_fe = feature_en()
-    ffm_transform(data_fe)
+    data, test_number, validation_number = feature_en()
+    ffm_transform(data, test_number, validation_number)
     train()
-    print('---------------prepare candidates--------------')
-    get_eachCityDiaryTop3000()
+    print("end")
+    # print('---------------prepare candidates--------------')
+    # get_eachCityDiaryTop3000()