
This is the Rprof() report by self time.
Is it also possible that these routines, which take long self.time, are causing the optim() to be slow?


$by.self
                        self.time self.pct total.time total.pct
"FUN"                       94.16     16.5      94.16      16.5
"unlist"                    80.46     14.1     120.54      21.1
"lapply"                    76.94     13.5     255.48      44.7
"match"                     60.76     10.6      60.88      10.7
"as.matrix.data.frame"      31.00      5.4      51.12       8.9
"as.character"              29.28      5.1      29.28       5.1
"unique.default"            24.36      4.3      24.40       4.3
"data.frame"                21.06      3.7      55.78       9.8
"split.default"             20.42      3.6      84.38      14.8
"tapply"                    13.84      2.4     414.28      72.5
"structure"                 11.32      2.0      22.36       3.9
"factor"                    11.08      1.9     127.68      22.3
"attributes<-"              11.00      1.9      11.00       1.9
"=="                        10.56      1.8      10.56       1.8
"%*%"                       10.30      1.8      10.30       1.8
"as.vector"                 10.22      1.8      10.22       1.8
"as.integer"                 9.86      1.7       9.86       1.7
"list"                       9.64      1.7       9.64       1.7
"exp"                        7.12      1.2       7.12       1.2
"as.data.frame.integer"      5.98      1.0       8.10       1.4

> To: bbom419@hotmail.com
> CC: jholtman@gmail.com; r-help@r-project.org
> Subject: Re: [R] avoiding loop
> From: mtmorgan@fhcrc.org
> Date: Sun, 1 Nov 2009 22:14:09 -0800
> 
> parkbomee <bbom419@hotmail.com> writes:
> 
> > Thank you all.
> >
> > What Chuck has suggested might not be applicable since the number of
> > different times is around 40,000.
> >
> > The object of optimization in my function is the varying "value",
> > which is basically data * parameter, of which "parameter" is the
> > object of optimization..
> >  
> > And from the r profiling with a subset of data,
> > I got this report..any idea what "<Anonymous>" is?
> >
> >
> > $by.total
> >                         total.time total.pct self.time self.pct
> > "<Anonymous>"               571.56     100.0      0.02      0.0
> > "optim"                     571.56     100.0      0.00      0.0
> > "fn"                        571.54     100.0      0.98      0.2
> 
> You're giving us 'by.total', so these are saying that all the time was
> spent in these functions or the functions they called. Probably all
> are in 'optim' and its arguments; since little self.time is spent
> here, there isn't much to work with
> 
> > "eval"                      423.74      74.1      0.00      0.0
> > "with.default"              423.74      74.1      0.00      0.0
> > "with"                      423.74      74.1      0.00      0.0
> 
> These are probably in the internals of optim, where the function
> you're trying to optimize is being set up for evaluation. Again
> there's little self.time, and all these say is that a big piece of the
> time is being spent in code called by this code.
> 
> > "tapply"                    414.28      72.5     13.84      2.4
> > "lapply"                    255.48      44.7     76.94     13.5
> > "factor"                    127.68      22.3     11.08      1.9
> > "unlist"                    120.54      21.1     80.46     14.1
> > "FUN"                        94.16      16.5     94.16     16.5
> 
> these look like they are tapply-related calls (looking at the code for
> tapply, it calls lapply, factor, and unlist, and FUN is the function
> argument to tapply), perhaps from the function you're optimizing (did
> you implement this as suggested below?  it would really help to have a
> possibly simplified version of the code you're calling).
> 
> There is material to work with here, as apparently a fairly large
> amount of self.time is being spent in each of these functions. So
> here's a sample data set
> 
>   n <- 100000
>   set.seed(123)
>   df <- data.frame(time=sort(as.integer(ceiling(runif(n)*n/5))),
>                    value=ceiling(runif(n)*5))
> 
> It would have been helpful for you to provide reproducible code like
> that above, so that the characteristics of your data were easily
> reproducible. Let's time tapply
> 
> > replicate(5, {
> +     system.time(x0 <<- tapply0(df$value, df$time, sum), gcFirst=TRUE)[[1]]
> + })
> [1] 0.316 0.316 0.308 0.320 0.304
> 
> tapply is quite general, but in your case I think you'd be happy with
> 
>   tapply1 <- function(X, INDEX, FUN)
>       unlist(lapply(split(X, INDEX), FUN), use.names=FALSE)
> 
> > replicate(5, {
> +     system.time(x1 <<- tapply1(df$value, df$time, sum), gcFirst=TRUE)[[1]]
> + })
> [1] 0.156 0.148 0.152 0.144 0.152
> 
> so about twice the speed (timing depends quite a bit on what 'time' is,
> integer or numeric or character or factor). The vector values of the
> two calculations are identical, though tapply presents the data as an
> array with column names
> 
> > identical(as.vector(x0), x1)
> [1] TRUE
> 
> tapply allows FUN to be anything, but if the interest is in the sum of
> each time interval, and the time intervals can be assumed to be sorted
> (sorting is not expensive, so could be done on the fly), then
> 
>   tapply2 <- function(X, INDEX)
>   {
>       csum <- cumsum(c(0, X))
>       idx <- diff(INDEX) != 0
>       csum[c(FALSE, idx, TRUE)] - csum[c(TRUE, idx, FALSE)]
>   }
> 
> calculates the cumulative sum and the points in INDEX where the time
> intervals change. It then takes the difference over the appropriate
> interval.
> 
> > replicate(5, {
> +     system.time(x2 <<- tapply2(df$value, df$time), gcFirst=TRUE)[[1]]
> + })
> [1] 0.024 0.024 0.024 0.024 0.024
> > identical(as.vector(x0), x2)
> [1] TRUE
> 
> This approach could be subject to rounding error (if csum gets very
> large and the intervals remain small). To calculate values where
> choice == 1 I think you'd want to
> 
>   tapply2(df$value * (df$choice==1), df$time)
> 
> rather than sub-setting, so that the result of tapply2 is always a
> vector of the same length even when some time intervals never have
> choice==1.
> 
> Because tapply in these examples seems so fast compared to your
> calculation, I wonder whether optim is evaluating your function many
> times, and that reformulating the optimization might lead to a very
> substantial speed-up?
> 
> Martin
> 
> > .
> > .
> > .
> > .
> > .
> >
> >
> >> Date: Sun, 1 Nov 2009 15:35:41 -0400
> >> Subject: Re: [R] avoiding loop
> >> From: jholtman@gmail.com
> >> To: bbom419@hotmail.com
> >> CC: dwinsemius@comcast.net; d.rizopoulos@erasmusmc.nl; r-help@r-project.org
> >> 
> >> What you need to do is to understand how to use Rprof so that you can
> >> determine where the time is being spent.  It probably indicates that
> >> this is not the source of slowness in your optimization function.  How
> >> much time are we talking about?  You may spent more time trying to
> >> optimize the function than just running the current version even if it
> >> is "slow" (slow is a relative term and does not hold much meaning
> >> without some context round it).
> >> 
> >> On Sat, Oct 31, 2009 at 11:36 PM, parkbomee <bbom419@hotmail.com> wrote:
> >> >
> >> > Thank you both.
> >> >
> >> > However, using tapply() instead of a loop does not seem to improve my code much.
> >> > I am using this inside of an optimization function,
> >> > and it still takes more than it needs...
> >> >
> >> >
> >> >
> >> >> CC: bbom419@hotmail.com; r-help@r-project.org
> >> >> From: dwinsemius@comcast.net
> >> >> To: d.rizopoulos@erasmusmc.nl
> >> >> Subject: Re: [R] avoiding loop
> >> >> Date: Sat, 31 Oct 2009 22:26:17 -0400
> >> >>
> >> >> This is pretty much equivalent:
> >> >>
> >> >> tapply(DF$value[DF$choice==1], DF$time[DF$choice==1], sum) /
> >> >>          tapply(DF$value, DF$time, sum)
> >> >>
> >> >> And both will probably fail if the number of groups with choice==1 is
> >> >> different than the number overall.
> >> >>
> >> >> --
> >> >> David.
> >> >>
> >> >> On Oct 31, 2009, at 5:14 PM, Dimitris Rizopoulos wrote:
> >> >>
> >> >> > one approach is the following:
> >> >> >
> >> >> > # say 'DF' is your data frame, then
> >> >> > with(DF, {
> >> >> >    ind <- choice == 1
> >> >> >    n <- tapply(value[ind], time[ind], sum)
> >> >> >    d <- tapply(value, time, sum)
> >> >> >    n / d
> >> >> > })
> >> >> >
> >> >> >
> >> >> > I hope it helps.
> >> >> >
> >> >> > Best,
> >> >> > Dimitris
> >> >> >
> >> >> >
> >> >> > parkbomee wrote:
> >> >> >> Hi all,
> >> >> >> I am trying to figure out a way to improve my code's efficiency by
> >> >> >> avoiding the use of loop.
> >> >> >> I want to calculate a conditional mean(?) given time.
> >> >> >> For example, from the data below, I want to calculate sum((value|
> >> >> >> choice==1)/sum(value)) across time.
> >> >> >> Is there a way to do it without using a loop?
> >> >> >> time  cum_time  choice    value
> >> >> >> 1         4             1           3
> >> >> >> 1         4              0           2
> >> >> >> 1          4             0           3
> >> >> >> 1          4             0           3
> >> >> >> 2         6             1           4
> >> >> >> 2         6             0           4
> >> >> >> 2         6             0           2
> >> >> >> 2         6             0           4
> >> >> >> 2         6             0           2
> >> >> >> 2         6             0           2 3         4
> >> >> >> 1           2 3         4             0           3 3
> >> >> >> 4             0           5 3         4             0           2
> >> >> >> My code looks like
> >> >> >> objective[1] = value[1] / sum(value[1:cum_time[1])
> >> >> >> for (i in 2:max(time)){
> >> >> >>     objective[i] = value[cum_time[i-1]+1] /
> >> >> >> sum(value[(cum_time[i-1]+1) : cum_time[i])])
> >> >> >> }
> >> >> >> sum(objective)
> >> >> >> Anyone have an idea that I can do this without using a loop??
> >> >> >> Thanks.
> >> >> >>
> >> >> >> _________________________________________________________________
> >> >> >> [[elided Hotmail spam]]
> >> >> >>    [[alternative HTML version deleted]]
> >> >> >> ______________________________________________
> >> >> >> R-help@r-project.org mailing list
> >> >> >> https://stat.ethz.ch/mailman/listinfo/r-help
> >> >> >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> >> >> >> and provide commented, minimal, self-contained, reproducible code.
> >> >> >
> >> >> > --
> >> >> > Dimitris Rizopoulos
> >> >> > Assistant Professor
> >> >> > Department of Biostatistics
> >> >> > Erasmus University Medical Center
> >> >> >
> >> >> > Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
> >> >> > Tel: +31/(0)10/7043478
> >> >> > Fax: +31/(0)10/7043014
> >> >> >
> >> >> > ______________________________________________
> >> >> > R-help@r-project.org mailing list
> >> >> > https://stat.ethz.ch/mailman/listinfo/r-help
> >> >> > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> >> >> > and provide commented, minimal, self-contained, reproducible code.
> >> >>
> >> >> David Winsemius, MD
> >> >> Heritage Laboratories
> >> >> West Hartford, CT
> >> >>
> >> >
> >> > _________________________________________________________________
> >> > [[elided Hotmail spam]]
> >> >        [[alternative HTML version deleted]]
> >> >
> >> > ______________________________________________
> >> > R-help@r-project.org mailing list
> >> > https://stat.ethz.ch/mailman/listinfo/r-help
> >> > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> >> > and provide commented, minimal, self-contained, reproducible code.
> >> >
> >> 
> >> 
> >> 
> >> -- 
> >> Jim Holtman
> >> Cincinnati, OH
> >> +1 513 646 9390
> >> 
> >> What is the problem that you are trying to solve?
> >  		 	   		  
> > _________________________________________________________________
> > [[elided Hotmail spam]]
> >
> > 	[[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> 
> -- 
> Martin Morgan
> Computational Biology / Fred Hutchinson Cancer Research Center
> 1100 Fairview Ave. N.
> PO Box 19024 Seattle, WA 98109
> 
> Location: Arnold Building M1 B861
> Phone: (206) 667-2793
 		 	   		  
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