[Bioc-devel] reproducible with mclapply?
Vincent Carey
stvjc at channing.harvard.edu
Thu Jun 4 16:39:33 CEST 2015
It does appear to me that the doRNG vignette sec 1.1 describes a solution
to the problem posed. It is less clear to me that this method is readily
adopted with BiocParallel unless registerDoPar is in use.... Should we
address this topic explicitly in the vignette?
On Thu, Jun 4, 2015 at 9:50 AM, Kasper Daniel Hansen <
kasperdanielhansen at gmail.com> wrote:
> Note you're not guaranteed that two random streams starting with different
> seeds will be (approximately) independent, so the suggestion on SO makes
> the numbers reproducible but technically wrong.
>
> If you want true independence you either need to use a parallel version of
> the random number generator or you do what I suggested. Because of how
> mclapply works (via fork) it is not clear to me that it is possible to use
> a parallel version of the random number generator, but I am not sure about
> this. The snippet from the documentation quoted above suggests I am wrong.
>
> Best,
> Kasper
>
> On Wed, Jun 3, 2015 at 11:25 PM, Vladislav Petyuk <petyuk at gmail.com>
> wrote:
>
> > There are different ways set.seed can be used. The way it is suggested
> on
> > the aforementioned stackoverflow post is basically a two stage process.
> > First seed is provided by a user (set.seed(1)). That is user can change
> > the outcome from run to run. Based on that seed, a vector of randomized
> > seeds is generated (seeds <- sample.int(length(input), replace=TRUE)).
> > Those seeds are basically arguments to the function under mclapply/lapply
> > that help to control random number generation for each iteration
> (set.seed
> > (seeds[idx])).
> > There are two different roles of set.seed. First left the user to control
> > random number generation and the second (within the function) makes sure
> > that it is the same for individual iterations regardless how the loop is
> > executed.
> > Does that make sense?
> >
> > On Wed, Jun 3, 2015 at 7:07 PM, Yu, Guangchuang <gcyu at connect.hku.hk>
> > wrote:
> >
> >> There is one possible solution posted in
> >>
> >>
> http://stackoverflow.com/questions/30610375/how-to-run-permutations-using-mclapply-in-a-reproducible-way-regardless-of-numbe/30627984#30627984
> >> .
> >>
> >> As Kasper suggested, it's not a proper way to use set.seed inside a
> >> package.
> >>
> >> I suggest using a parameter for example seed=FALSE to disable the
> set.seed
> >> and if user want the result reproducible, e.g. in demonstration, set
> >> seed=TRUE explicitly and set.seed will be run inside the function.
> >>
> >> Bests,
> >> Guangchuang
> >>
> >> On Wed, Jun 3, 2015 at 8:42 PM, Kasper Daniel Hansen <
> >> kasperdanielhansen at gmail.com> wrote:
> >>
> >> > For this situation, generate the permutation indexes outside of the
> >> > mclapply, and the do mclapply over a list with the indices.
> >> >
> >> > And btw., please don't use set.seed inside a package; that control
> >> should
> >> > completely be left to the user.
> >> >
> >> > Best,
> >> > Kasper
> >> >
> >> > On Wed, Jun 3, 2015 at 7:08 AM, Vincent Carey <
> >> stvjc at channing.harvard.edu>
> >> > wrote:
> >> >
> >> >> This document indicates how to achieve reproducibility independent of
> >> the
> >> >> underlying physical environment.
> >> >>
> >> >> http://cran.r-project.org/web/packages/doRNG/vignettes/doRNG.pdf
> >> >>
> >> >> Let me know if that satisfies the question.
> >> >>
> >> >> On Wed, Jun 3, 2015 at 5:32 AM, Yu, Guangchuang <gcyu at connect.hku.hk
> >
> >> >> wrote:
> >> >>
> >> >> > Der Vincent,
> >> >> >
> >> >> > RNGkind("L'Ecuyer-CMRG") works as using mc.set.seed=FALSE.
> >> >> >
> >> >> > When mc.cores changes, the output is not reproducible.
> >> >> >
> >> >> > I think this issue is also of concern within the Bioconductor
> >> community
> >> >> as parallel version of permutation test is commonly used now.
> >> >> >
> >> >> > Best Regards,
> >> >> >
> >> >> > Guangchuang
> >> >> >
> >> >> >
> >> >> >
> >> >> > On Wed, Jun 3, 2015 at 5:17 PM, Vincent Carey <
> >> >> stvjc at channing.harvard.edu>
> >> >> > wrote:
> >> >> >
> >> >> >> Hi, this question belongs on R-help, but perhaps
> >> >> >>
> >> >> >>
> >> >>
> >>
> https://stat.ethz.ch/R-manual/R-devel/library/parallel/html/RngStream.html
> >> >> >>
> >> >> >> will be useful.
> >> >> >>
> >> >> >> Best regards
> >> >> >>
> >> >> >> On Wed, Jun 3, 2015 at 3:11 AM, Yu, Guangchuang <
> >> gcyu at connect.hku.hk>
> >> >> >> wrote:
> >> >> >>
> >> >> >>> Dear all,
> >> >> >>>
> >> >> >>> I have an issue of setting seed value when using parallel
> package.
> >> >> >>>
> >> >> >>> > library("parallel")
> >> >> >>> > library("digest")
> >> >> >>> >
> >> >> >>> > set.seed(0)
> >> >> >>> > m <- mclapply(1:10, function(x) sample(1:10),
> >> >> >>> + mc.cores=2)
> >> >> >>> > digest(m, 'crc32')
> >> >> >>> [1] "4827c80c"
> >> >> >>> >
> >> >> >>> > set.seed(0)
> >> >> >>> > m <- mclapply(1:10, function(x) sample(1:10),
> >> >> >>> + mc.cores=2)
> >> >> >>> > digest(m, 'crc32')
> >> >> >>> [1] "e95b9134"
> >> >> >>>
> >> >> >>> By default, set.seed() will be ignored since mclapply will set
> the
> >> >> seed
> >> >> >>> internally.
> >> >> >>>
> >> >> >>> If we use mc.set.seed=FALSE to disable this feature. It works as
> >> >> >>> indicated
> >> >> >>> below:
> >> >> >>>
> >> >> >>> > set.seed(0)
> >> >> >>> > m <- mclapply(1:10, function(x) sample(1:10),
> >> >> >>> + mc.cores=2, mc.set.seed = FALSE)
> >> >> >>> > digest(m, 'crc32')
> >> >> >>> [1] "6bbada78"
> >> >> >>> >
> >> >> >>> > set.seed(0)
> >> >> >>> > m <- mclapply(1:10, function(x) sample(1:10),
> >> >> >>> + mc.cores=2, mc.set.seed = FALSE)
> >> >> >>> > digest(m, 'crc32')
> >> >> >>> [1] "6bbada78"
> >> >> >>>
> >> >> >>> The problems is that the results are also depending on the number
> >> of
> >> >> >>> cores.
> >> >> >>>
> >> >> >>> > set.seed(0)
> >> >> >>> > m <- mclapply(1:10, function(x) sample(1:10),
> >> >> >>> + mc.cores=4, mc.set.seed = FALSE)
> >> >> >>> > digest(m, 'crc32')
> >> >> >>> [1] "a22e0aab"
> >> >> >>>
> >> >> >>>
> >> >> >>> Any idea?
> >> >> >>>
> >> >> >>> Best Regards,
> >> >> >>> Guangchuang
> >> >> >>> --
> >> >> >>> --~--~---------~--~----~------------~-------~--~----~
> >> >> >>> Guangchuang Yu, PhD Candidate
> >> >> >>> State Key Laboratory of Emerging Infectious Diseases
> >> >> >>> School of Public Health
> >> >> >>> The University of Hong Kong
> >> >> >>> Hong Kong SAR, China
> >> >> >>> www: http://ygc.name
> >> >> >>> -~----------~----~----~----~------~----~------~--~---
> >> >> >>>
> >> >> >>> [[alternative HTML version deleted]]
> >> >> >>>
> >> >> >>> _______________________________________________
> >> >> >>> Bioc-devel at r-project.org mailing list
> >> >> >>> https://stat.ethz.ch/mailman/listinfo/bioc-devel
> >> >> >>>
> >> >> >>
> >> >> >>
> >> >> >
> >> >> >
> >> >> > --
> >> >> > --~--~---------~--~----~------------~-------~--~----~
> >> >> > Guangchuang Yu, PhD Candidate
> >> >> > State Key Laboratory of Emerging Infectious Diseases
> >> >> > School of Public Health
> >> >> > The University of Hong Kong
> >> >> > Hong Kong SAR, China
> >> >> > www: http://ygc.name
> >> >> > -~----------~----~----~----~------~----~------~--~---
> >> >> >
> >> >>
> >> >> [[alternative HTML version deleted]]
> >> >>
> >> >> _______________________________________________
> >> >> Bioc-devel at r-project.org mailing list
> >> >> https://stat.ethz.ch/mailman/listinfo/bioc-devel
> >> >>
> >> >
> >> >
> >>
> >>
> >> --
> >> --~--~---------~--~----~------------~-------~--~----~
> >> Guangchuang Yu, PhD Candidate
> >> State Key Laboratory of Emerging Infectious Diseases
> >> School of Public Health
> >> The University of Hong Kong
> >> Hong Kong SAR, China
> >> www: http://ygc.name
> >> -~----------~----~----~----~------~----~------~--~---
> >>
> >> [[alternative HTML version deleted]]
> >>
> >> _______________________________________________
> >> Bioc-devel at r-project.org mailing list
> >> https://stat.ethz.ch/mailman/listinfo/bioc-devel
> >>
> >
> >
>
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>
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