Hey Guoqiang and Sendong,
Could you comment on the overhead of calling gc() explicitly? The shuffle
files should get cleaned in a few seconds after checkpointing, but it is
certainly possible to accumulates TBs of files in a few seconds. In this
case, calling gc() may work the same as waiting for a few seconds after
each checkpoint. Is it correct?
Best,
Xiangrui
On Tue, Mar 31, 2015 at 8:58 AM, lisendong <lisend...@163.com> wrote:
> guoqiang ’s method works very well …
>
> it only takes 1TB disk now.
>
> thank you very much!
>
>
>
> 在 2015年3月31日，下午4:47，GuoQiang Li <wi...@qq.com> 写道：
>
> You can try to enforce garbage collection:
>
> /** Run GC and make sure it actually has run */
> def runGC() {
> val weakRef = new WeakReference(new Object())
> val startTime = System.currentTimeMillis
> System.gc() // Make a best effort to run the garbage collection. It
> *usually* runs GC.
> // Wait until a weak reference object has been GCed
> System.runFinalization()
> while (weakRef.get != null) {
> System.gc()
> System.runFinalization()
> Thread.sleep(200)
> if (System.currentTimeMillis - startTime > 10000) {
> throw new Exception("automatically cleanup error")
> }
> }
> }
>
>
> ------------------ 原始邮件 ------------------
> *发件人:* "lisendong"<lisend...@163.com>;
> *发送时间:* 2015年3月31日(星期二) 下午3:47
> *收件人:* "Xiangrui Meng"<men...@gmail.com>;
> *抄送:* "Xiangrui Meng"<m...@databricks.com>; "user"<user@spark.apache.org>;
> "Sean Owen"<so...@cloudera.com>; "GuoQiang Li"<wi...@qq.com>;
> *主题:* Re: different result from implicit ALS with explicit ALS
>
> I have update my spark source code to 1.3.1.
>
> the checkpoint works well.
>
> BUT the shuffle data still could not be delete automatically…the disk
> usage is still 30TB…
>
> I have set the spark.cleaner.referenceTracking.blocking.shuffle to true.
>
> Do you know how to solve my problem?
>
> Sendong Li
>
>
>
> 在 2015年3月31日，上午12:11，Xiangrui Meng <men...@gmail.com> 写道：
>
> setCheckpointInterval was added in the current master and branch-1.3.
> Please help check whether it works. It will be included in the 1.3.1 and
> 1.4.0 release. -Xiangrui
>
> On Mon, Mar 30, 2015 at 7:27 AM, lisendong <lisend...@163.com> wrote:
>
>> hi, xiangrui:
>> I found the ALS of spark 1.3.0 forget to do checkpoint() in explicit ALS:
>> the code is :
>>
>> https://github.com/apache/spark/blob/db34690466d67f9c8ac6a145fddb5f7ea30a8d8d/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
>> <PastedGraphic-2.tiff>
>>
>> the checkpoint is very important in my situation, because my task will
>> produce 1TB shuffle data in each iteration, it the shuffle data is not
>> deleted in each iteration(using checkpoint()), the task will produce 30TB
>> data…
>>
>>
>> So I change the ALS code, and re-compile by myself, but it seems the
>> checkpoint does not take effects, and the task still occupy 30TB disk… ( I
>> only add two lines to the ALS.scala) :
>>
>> <PastedGraphic-3.tiff>
>>
>>
>>
>> and the driver’s log seems strange, why the log is printed together...
>> <PastedGraphic-1.tiff>
>>
>> thank you very much!
>>
>>
>> 在 2015年2月26日，下午11:33，163 <lisend...@163.com> 写道：
>>
>> Thank you very much for your opinion:)
>>
>> In our case, maybe it 's dangerous to treat un-observed item as negative
>> interaction(although we could give them small confidence, I think they are
>> still incredible...)
>>
>> I will do more experiments and give you feedback:)
>>
>> Thank you;)
>>
>>
>> 在 2015年2月26日，23:16，Sean Owen <so...@cloudera.com> 写道：
>>
>> I believe that's right, and is what I was getting at. yes the implicit
>> formulation ends up implicitly including every possible interaction in
>> its loss function, even unobserved ones. That could be the difference.
>>
>> This is mostly an academic question though. In practice, you have
>> click-like data and should be using the implicit version for sure.
>>
>> However you can give negative implicit feedback to the model. You
>> could consider no-click as a mild, observed, negative interaction.
>> That is: supply a small negative value for these cases. Unobserved
>> pairs are not part of the data set. I'd be careful about assuming the
>> lack of an action carries signal.
>>
>> On Thu, Feb 26, 2015 at 3:07 PM, 163 <lisend...@163.com> wrote:
>> oh my god, I think I understood...
>> In my case, there are three kinds of user-item pairs:
>>
>> Display and click pair(positive pair)
>> Display but no-click pair(negative pair)
>> No-display pair(unobserved pair)
>>
>> Explicit ALS only consider the first and the second kinds
>> But implicit ALS consider all the three kinds of pair(and consider the
>> third
>> kind as the second pair, because their preference value are all zero and
>> confidence are all 1)
>>
>> So the result are different. right?
>>
>> Could you please give me some advice, which ALS should I use?
>> If I use the implicit ALS, how to distinguish the second and the third
>> kind
>> of pair:)
>>
>> My opinion is in my case, I should use explicit ALS ...
>>
>> Thank you so much
>>
>> 在 2015年2月26日，22:41，Xiangrui Meng <m...@databricks.com> 写道：
>>
>> Lisen, did you use all m-by-n pairs during training? Implicit model
>> penalizes unobserved ratings, while explicit model doesn't. -Xiangrui
>>
>> On Feb 26, 2015 6:26 AM, "Sean Owen" <so...@cloudera.com> wrote:
>>
>> +user
>>
>> On Thu, Feb 26, 2015 at 2:26 PM, Sean Owen <so...@cloudera.com> wrote:
>>
>> I think I may have it backwards, and that you are correct to keep the 0
>> elements in train() in order to try to reproduce the same result.
>>
>> The second formulation is called 'weighted regularization' and is used
>> for both implicit and explicit feedback, as far as I can see in the code.
>>
>> Hm, I'm actually not clear why these would produce different results.
>> Different code paths are used to be sure, but I'm not yet sure why they
>> would give different results.
>>
>> In general you wouldn't use train() for data like this though, and would
>> never set alpha=0.
>>
>> On Thu, Feb 26, 2015 at 2:15 PM, lisendong <lisend...@163.com> wrote:
>>
>> I want to confirm the loss function you use (sorry I’m not so familiar
>> with scala code so I did not understand the source code of mllib)
>>
>> According to the papers :
>>
>>
>> in your implicit feedback ALS, the loss function is (ICDM 2008):
>>
>> in the explicit feedback ALS, the loss function is (Netflix 2008):
>>
>> note that besides the difference of confidence parameter Cui, the
>> regularization is also different. does your code also has this
>> difference?
>>
>> Best Regards,
>> Sendong Li
>>
>>
>> 在 2015年2月26日，下午9:42，lisendong <lisend...@163.com> 写道：
>>
>> Hi meng, fotero, sowen:
>>
>> I’m using ALS with spark 1.0.0, the code should be:
>>
>>
>> https://github.com/apache/spark/blob/branch-1.0/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
>>
>> I think the following two method should produce the same (or near)
>> result:
>>
>> MatrixFactorizationModel model = ALS.train(ratings.rdd(), 30, 30, 0.01,
>> -1, 1);
>>
>> MatrixFactorizationModel model = ALS.trainImplicit(ratings.rdd(), 30,
>> 30, 0.01, -1, 0, 1);
>>
>> the data I used is display log, the format of log is as following:
>>
>> user item if-click
>>
>>
>>
>>
>>
>>
>> I use 1.0 as score for click pair, and 0 as score for non-click pair.
>>
>> in the second method, the alpha is set to zero, so the confidence for
>> positive and negative are both 1.0 (right?)
>>
>> I think the two method should produce similar result, but the result is
>> : the second method’s result is very bad (the AUC of the first result is
>> 0.7, but the AUC of the second result is only 0.61)
>>
>>
>> I could not understand why, could you help me?
>>
>>
>> Thank you very much!
>>
>> Best Regards,
>> Sendong Li
>>
>>
>>
>>
>>
>>
>>
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