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From Koobas <koo...@gmail.com>
Subject Re: Consistent repeatable results for distributed ALS-WR recommender
Date Mon, 24 Jun 2013 21:29:40 GMT
On Mon, Jun 24, 2013 at 5:07 PM, Dmitriy Lyubimov <dlieu.7@gmail.com> wrote:

> On Mon, Jun 24, 2013 at 1:35 PM, Michael Kazekin <kazmikh@hotmail.com
> >wrote:
>
> > I agree with you, I should have mentioned earlier that it would be good
> to
> > separate "noise from data" and deal with only what is separable. Of
> course
> > there is no truly deterministic implementation of any algorithm,
>
>
> I am pretty sure "2.0 + 2.0" is pretty deterministic  :)
>
>
Few things are naturally deterministic in parallel computing.
Many parallel sorting algorithms are non-deterministic.

In floating point commutativity is gone.
So, while 2.0 + 2.0 is deterministic, 1.0 + 10.0 + 100.0 is not 1.0 + 100.0
+ 10.0.
Again, you don't know what happens if the reduction is done in parallel.



> > but I would expect to see "credible" results on a macro-level (in our
> case
> > it would be nice to see the same order of recommendations given the fixed
> > seed). It seems important for experiments (and for testing, as
> mentioned),
> > isn't it?
> >
>
> Yes for unit tests you usually would want to fix the seed if it means that
> assertion may fail  with a non-zero probability. There are definitely a lot
> of such cases in Mahout.
>
> Another question is that afaik ALS-WR is deterministic by its inception, so
> > I'm trying to understand the reasons (and I'm assuming there are some)
> for
> > the specific implementation design.
> >
> > Thanks for a free lunch! ;)
> > Cheers,Mike.
> >
> > > Date: Mon, 24 Jun 2013 13:13:20 -0700
> > > Subject: Re: Consistent repeatable results for distributed ALS-WR
> > recommender
> > > From: dlieu.7@gmail.com
> > > To: user@mahout.apache.org
> > >
> > > On Mon, Jun 24, 2013 at 1:07 PM, Michael Kazekin <kazmikh@hotmail.com
> > >wrote:
> > >
> > > > Thank you, Ted!
> > > > Any feedback on the usefulness of such functionality? Could it
> increase
> > > > the 'playability' of the recommender?
> > > >
> > >
> > > Almost all methods -- even deterministic ones -- will have a "credible
> > > interval" of prediction simply because method assumptions do not hold
> > 100%
> > > in real life, real data. So what you really want to know in such cases
> is
> > > the credible interval rather than whether method is deterministic or
> not.
> > > Non-deterministic methods might very well be more accurate than
> > > deterministic ones in this context, and, therefore, more "useful". Also
> > > see: "no free lunch theorem".
> > >
> > >
> > > > > From: ted.dunning@gmail.com
> > > > > Date: Mon, 24 Jun 2013 20:46:43 +0100
> > > > > Subject: Re: Consistent repeatable results for distributed ALS-WR
> > > > recommender
> > > > > To: user@mahout.apache.org
> > > > >
> > > > > See org.apache.mahout.common.RandomUtils#useTestSeed
> > > > >
> > > > > It provides the ability to freeze the initial seed.  Normally this
> is
> > > > only
> > > > > used during testing, but you could use it.
> > > > >
> > > > >
> > > > > On Mon, Jun 24, 2013 at 8:44 PM, Michael Kazekin <
> > kazmikh@hotmail.com
> > > > >wrote:
> > > > >
> > > > > > Thanks a lot!
> > > > > > Do you know by any chance what are the underlying reasons for
> > including
> > > > > > such mandatory random seed initialization?
> > > > > > Do you see any sense in providing another option, such as filling
> > them
> > > > > > with zeroes in order to ensure the consistency and repeatability?
> > (for
> > > > > > example we might want to track and compare the generated
> > recommendation
> > > > > > lists for different parameters, such as the number of features
or
> > > > number of
> > > > > > iterations etc.)
> > > > > > M.
> > > > > >
> > > > > >
> > > > > > > Date: Mon, 24 Jun 2013 19:51:44 +0200
> > > > > > > Subject: Re: Consistent repeatable results for distributed
> ALS-WR
> > > > > > recommender
> > > > > > > From: ssc@apache.org
> > > > > > > To: user@mahout.apache.org
> > > > > > >
> > > > > > > The matrices of the factorization are initalized randomly.
If
> you
> > > > fix the
> > > > > > > random seed (would require modification of the code) you
should
> > get
> > > > > > exactly
> > > > > > > the same results.
> > > > > > > Am 24.06.2013 13:49 schrieb "Michael Kazekin" <
> > kazmikh@hotmail.com>:
> > > > > > >
> > > > > > > > Hi!
> > > > > > > > Should I assume that under same dataset and same parameters
> for
> > > > > > factorizer
> > > > > > > > and recommender I will get the same results for any
specific
> > user?
> > > > > > > > My current understanding that theoretically ALS-WR
algorithm
> > could
> > > > > > > > guarantee this, but I was wondering could be there
any
> numeric
> > > > method
> > > > > > > > issues and/or implementation-specific concerns.
> > > > > > > > Would appreciate any highlight on this issue.
> > > > > > > > Mike.
> > > > > > > >
> > > > > > > >
> > > > > > > >
> > > > > >
> > > >
> > > >
> >
> >
>

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