delete predict function param argument

diaryTraining.py

@@ -15,7 +15,6 @@ def train():
     print("predicting")
     ffm_model.setTest(DIRECTORY_PATH + "test_ffm_data.csv")
     ffm_model.setSigmoid()
-    param_predict = {"log": "/data2/models/result"}
-    ffm_model.predict(param_predict,DIRECTORY_PATH + "model.out",DIRECTORY_PATH + "test_set_predict_output.txt")
+    ffm_model.predict(DIRECTORY_PATH + "model.out",DIRECTORY_PATH + "test_set_predict_output.txt")
 
 

predictDiary.py

@@ -48,9 +48,7 @@ def predict(user_profile):
     ffm_model.setTest(instance_file_path)
     ffm_model.setSigmoid()
 
-    #日志保存路径，如果不加这个参数，日志默认保存在/temp路径下，不符合规范
-    param = {"log": "/data2/models/result"}
-    ffm_model.predict(param,DIRECTORY_PATH + "model.out",
+    ffm_model.predict(DIRECTORY_PATH + "model.out",
                       DIRECTORY_PATH + "result/{0}_output.txt".format(user_profile['device_id']))
     print("该用户预测结束")
     predict_save_to_local(user_profile, instance)

utils.py

@@ -94,7 +94,7 @@ class multiFFMFormatPandas:
         self.fit(df, y)
         n = n
         processes = processes
-        return self.transform(df,n=n,processes=processes)
+        return self.transform(df,n,processes)
 
     def transform_row_(self, row, t):
         ffm = []
@@ -112,17 +112,13 @@ class multiFFMFormatPandas:
                 ffm.append('{}:{}:{}'.format(self.field_index_[col], self.feature_index_[col], val))
         return ' '.join(ffm)
 
-    # def transform(self, df):
-    #     t = df.dtypes.to_dict()
-    #     return pd.Series({idx: self.transform_row_(row, t) for idx, row in df.iterrows()})
-
     def transform(self, df,n=10000,processes=1):
         # n是每个线程运行最大的数据条数,processes是线程数
         t = df.dtypes.to_dict()
         data_list = self.data_split_line(df,n)
 
         # 设置进程的数量
-        pool = Pool(processes=processes)
+        pool = Pool(processes)
         for i in range(len(data_list)):
             print("处理进度: "+str(i+1)+"/"+str(len(data_list)))
             data_list[i] = pool.apply_async(self.pool_function, (data_list[i], t,))