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License: MIT License
请问,我看了在taobao的训练数据中,数据得预处理,其中行为类型好像没有用到?是这样吗?另外时间数据,只是作为一个排序来使用的,并没有考虑行为之间的时间差是吗?
UserBehavior.csv共3G,跑了一晚上还是没完成,早上看卡在了3一动不动:
D:\Anaconda3\envs\TF2\python.exe F:/python/MIMN-master/preprocess/taobao_prepare.py
4162024 987994 9439 4
feature_size 4162024 5159462
group completed
987994
get user last touch time completed
1
2
3
然后一个白天也一动不动。有没有童鞋们把mimn跑起来呢?本想改成TF2的版本的,发现_Linear比较麻烦,然后放弃了
import random
fi = open("local_test", "r")
ftrain = open("local_train_splitByUser", "w")
ftest = open("local_test_splitByUser", "w")
while True:
rand_int = random.randint(1, 10)
noclk_line = fi.readline().strip()
clk_line = fi.readline().strip()
if noclk_line == "" or clk_line == "":
break
if rand_int == 2:
print >> ftest, noclk_line
print >> ftest, clk_line
else:
print >> ftrain, noclk_line
print >> ftrain, clk_line
这个脚本对测试集划分为train和test,写的有问题吧? 不过看起来之前的步骤local_aggretor.py里就已经划分好了吧,
I think "i==1" in mimn.py Line84 should be changed by "i>=self.read_head_num".
From my point of view, this "i==1" is used to limit the operation only on write head weight when the self.read_head_num==1 and self.write_head_num==1.
But these two parameter can be changes in the code, so this "i==1" can cause problem when self.read_head_num!=1 or self.write_head_num!=1.
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