It so happens I have been looking at very similar changes, as well as
adding multi-threading support for dist(); these should make it into
R-devel later this summer.
Best,
luke
On Sun, 17 Jun 2012, Stefan Evert wrote:
I'm working on analyzing a large data set, lets asume that
dim(Data)=c(1000,8700). I want to calculate the canberra distance
between the columns of this matrix, and using a toy example ('test' is
a matrix filled with random numbers 0-1):
system.time(d<-as.matrix(dist(t(test), method = "canberra", diag = FALSE, upper
= FALSE, p = 2)))
user system elapsed
1417.713 3.219 1421.144
The system.time results also confuse me a bit, since 99% of the time
is not system time but user time. What does that mean?
User time is the time that R spends working on your problem; system time refers
to tasks done by the operating system, e.g. disk access, managing locks and,
most importantly, swapping when you run out of RAM. With multi-threading,
system time can be much larger than the time that has actually elapsed.
Is there any way to calculate the distance which would take less time?
Well, one thing you can do is to get a faster computer. :-) The command above
takes only 670 seconds on my MacBook Pro (without multi-threading).
Calculating a distance matrix is an expensive computation. In your example, R
has to carry out (8700 * 8700 * 1000) / 2 = 37.8 billion floating point
divisions. With approx. 27 clock cycles per division (according to tables I've
found on the Web), this takes at least 340 seconds even on a 3GHz CPU (and
ignoring memory access, addition/subtraction, loops, etc.).
You can shave off some of the time if you implement the distance calculation in
C, inline the code to avoid callback functions in loops, operate on columns of
the matrix directly (which should be more cache-friendly than rows) and don't
check for NA's, NaN's and other degenerate cases.
I've done just that in my experimental R package "wordspace", which isn't on
CRAN yet:
library(wordspace)
A <- matrix(runif(8.7e6), 1000, 8700)
system.time(d1 <- as.matrix(dist(t(A), method="canberra")))
user system elapsed
669.207 2.724 669.305
system.time(d2 <- dist.matrix(A, method="canberra", byrow=FALSE))
user system elapsed
250.534 0.784 250.301
all(d1 == d2)
[1] TRUE
If you aren't tied to Canberra distance, you can use a less expensive metric
such as the Manhattan distance for an additional, more substantial speed boost:
system.time(d3 <- dist.matrix(A, method="manhattan", byrow=FALSE))
user system elapsed
42.488 0.999 43.569
This is still single-threaded, so you can run multiple of these calculations in
parallel depending on how many cores your server has.
Hope this helps,
Stefan
PS: In case you'd like to give it a try yourself and aren't daunted by a
complete lack of documentation:
svn checkout svn://scm.r-forge.r-project.org/svnroot/wordspace/pkg
[ ev...@linglit.tu-darmstadt.de | http://purl.org/stefan.evert ]
______________________________________________
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.
--
Luke Tierney
Chair, Statistics and Actuarial Science
Ralph E. Wareham Professor of Mathematical Sciences
University of Iowa Phone: 319-335-3386
Department of Statistics and Fax: 319-335-3017
Actuarial Science
241 Schaeffer Hall email: luke-tier...@uiowa.edu
Iowa City, IA 52242 WWW: http://www.stat.uiowa.edu
______________________________________________
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.
