add print

diary-training.py

@@ -4,6 +4,8 @@ import pandas as pd
 from sklearn.utils import shuffle
 import numpy as np
 import xlearn as xl
+
+
 # 从数据库的表里获取数据，并转化成df格式
 def con_sql(sql):
     db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
@@ -48,13 +50,13 @@ data = click.append(exposure)
 print("done 合并点击表和曝光表")
 print(data.head(2))
 # 从time特征中抽取hour、weekday
-data["hour"] = data["time"].apply(lambda x:datetime.datetime.fromtimestamp(x).hour)
-data["weekday"] = data["time"].apply(lambda x:datetime.datetime.fromtimestamp(x).weekday())
+# data["hour"] = data["time"].apply(lambda x:datetime.datetime.fromtimestamp(x).hour)
+# data["weekday"] = data["time"].apply(lambda x:datetime.datetime.fromtimestamp(x).weekday())
 # 数值是0的特征会被ffm格式删除，经过下面的处理后，没有数值是0的特征
-data.loc[data["hour"]==0] = 24
-data.loc[data["weekday"]==0] = 7
-data["hour"] = data["hour"].astype("category")
-data["weekday"] = data["weekday"].astype("category")
+# data.loc[data["hour"]==0] = 24
+# data.loc[data["weekday"]==0] = 7
+# data["hour"] = data["hour"].astype("category")
+# data["weekday"] = data["weekday"].astype("category")
 data = data.drop("time",axis=1)
 print("成功从time特征中抽取hour、weekday")
 print(data.head(2))

utils.py

@@ -59,13 +59,13 @@ def queue_compare(old_list, new_list):
     x_dict = dict(zip(old_list, temp))
     temp = list(range(len(new_list)))
     y_dict = dict(zip(new_list, temp))
-
     i = 0
     for key in x_dict.keys():
         if x_dict[key] != y_dict[key]:
             i += 1
-    print("日记队列更新前日记总个数{}，位置发生变化个数{}，发生变化率{}%".format(len(old_list), i,
-                                                      round(i / len(old_list) * 100), 2))
+    if i > 0:
+        print("日记队列更新前日记总个数{}，位置发生变化个数{}，发生变化率{}%".format(len(old_list), i,
+                                                          round(i / len(old_list) * 100), 2))
 
 
 def move_file():