From hb at biostat.ucsf.edu  Thu Mar  5 20:39:45 2015
From: hb at biostat.ucsf.edu (Henrik Bengtsson)
Date: Thu, 5 Mar 2015 11:39:45 -0800
Subject: [Rd] Performance issue in stats:::weighted.mean.default method
In-Reply-To: <54F89747.70609@stats.ox.ac.uk>
References: <54F86E53.4050809@palusga.cz> <54F89747.70609@stats.ox.ac.uk>
Message-ID: <CAFDcVCTk2y0NLQy53mwcR3a-OMXy7utEWbRtje3x3me=EG91hA@mail.gmail.com>

See weightedMean() in the matrixStats package.  It's optimized for
data type, speed and memory and implemented in native code so it can
avoid some of these intermediate copies.  It's a few times faster than
weighted.mean[.default]();

library(matrixStats)
library(microbenchmark)
n <- 5000
x <- sample(500,n,replace=TRUE)
w <- sample(1000,n,replace=TRUE)/1000 *
ifelse((sample(10,n,replace=TRUE) -1) > 0, 1, 0)
fun.new <- function(x,w) {sum(x*w)/sum(w)}
fun.orig  <- function(x,w) {sum(x*w[w!=0])/sum(w)}
stats <- microbenchmark(
  weightedMean(x,w),
  weighted.mean(x,w),
  ORIGFN = fun.orig(x,w),
  NEWFN  = fun.new(x,w),
  times = 1000
)

> print(stats, digits=3)
Unit: microseconds
                expr   min    lq  mean median    uq    max neval
  weightedMean(x, w)  28.7  31.7  33.4   32.9  33.8   81.7  1000
 weighted.mean(x, w) 129.6 141.6 149.6  143.7 147.1 2332.9  1000
              ORIGFN 205.7 222.0 235.0  225.4 231.4 2655.8  1000
               NEWFN  38.9  42.3  44.3   42.8  43.6  385.8  1000

Relative performance will vary with n = length(x).

The weightedMean() function handles zero-weight Inf values:

> w <- c(0, 1)
> x <- c(Inf, 1)
> weighted.mean(x, w)
[1] 1
> fun.new(x, w)
[1] NaN
> weightedMean(x,w)
[1] 1

You'll find more benchmark results on weightedMean() vs
weighted.mean() on
https://github.com/HenrikBengtsson/matrixStats/wiki/weightedMean

/Henrik

On Thu, Mar 5, 2015 at 9:49 AM, Prof Brian Ripley <ripley at stats.ox.ac.uk> wrote:
> On 05/03/2015 14:55, Tade?? Palusga wrote:
>>
>> Hi,
>>    I'm using this mailing list for the first time and I hope this is the
>> right one. I don't think that the following is a bug but it can be a
>> performance issue.
>>
>>     By my opinion, there is no need to filter by [w != 0] in last sum of
>> weighted.mean.default method defined in
>> src/library/stats/R/weighted.mean.R. There is no need to do it because
>> you can always sum zero numbers and filtering is too expensive (see
>> following benchmark snippet)
>
>
> But 0*x is not necessarily 0, so there is a need to do it ... see
>
>> w <- c(0, 1)
>> x <- c(Inf, 1)
>> weighted.mean(x, w)
> [1] 1
>> fun.new(x, w)
> [1] NaN
>
>
>>
>>
>>
>> library(microbenchmark)
>> x <- sample(500,5000,replace=TRUE)
>> w <- sample(1000,5000,replace=TRUE)/1000 *
>> ifelse((sample(10,5000,replace=TRUE) -1) > 0, 1, 0)
>> fun.new <- function(x,w) {sum(x*w)/sum(w)}
>> fun.orig  <- function(x,w) {sum(x*w[w!=0])/sum(w)}
>> print(microbenchmark(
>>    ORIGFN = fun.orig(x,w),
>>    NEWFN  = fun.new(x,w),
>>    times = 1000))
>>
>> #results:
>> #Unit: microseconds
>> #   expr     min       lq      mean  median      uq      max neval
>> # ORIGFN 190.889 194.6590 210.08952 198.847 202.928 1779.789  1000
>> #  NEWFN  20.857  21.7175  24.61149  22.080  22.594 1744.014  1000
>>
>>
>>
>>
>> So my suggestion is to remove the w != check
>>
>>
>>
>>
>> Index: weighted.mean.R
>> ===================================================================
>> --- weighted.mean.R     (revision 67941)
>> +++ weighted.mean.R     (working copy)
>> @@ -29,7 +29,7 @@
>>           stop("'x' and 'w' must have the same length")
>>       w <- as.double(w) # avoid overflow in sum for integer weights.
>>       if (na.rm) { i <- !is.na(x); w <- w[i]; x <- x[i] }
>> -    sum((x*w)[w != 0])/sum(w) # --> NaN in empty case
>> +    sum(x*w)/sum(w) # --> NaN in empty case
>>   }
>>
>>   ## see note for ?mean.Date
>>
>>
>> I hope i'm not missing something - I really don't see the reason to have
>> this filtration here.
>>
>> BR
>>
>> Tadeas 'donarus' Palusga
>>
>> ______________________________________________
>> R-devel at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-devel
>
>
>
> --
> Brian D. Ripley,                  ripley at stats.ox.ac.uk
> Emeritus Professor of Applied Statistics, University of Oxford
> 1 South Parks Road, Oxford OX1 3TG, UK
>
>
> ______________________________________________
> R-devel at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-devel



[Rd] Performance issue in stats:::weighted.mean.default method
Henrik Bengtsson