[R-pkgs] matrixStats 0.13.1 - Methods that Apply to Rows and Columns of a Matrix (and Vectors)
Henrik Bengtsson
hb at biostat.ucsf.edu
Mon Jan 26 22:19:59 CET 2015
A new release 0.13.1 of matrixStats is now on CRAN
[http://cran.r-project.org/package=matrixStats]. The source code is
available on GitHub [https://github.com/HenrikBengtsson/matrixStats].
WHAT DOES IT DO?
The matrixStats package provides highly optimized functions for
computing common summaries over rows and columns of matrices,
e.g.rowQuantiles(). There are also functions that operate on vectors,
e.g. logSumExp(). Their implementations strive to minimize both memory
usage and processing time. They are often remarkably faster compared
to good old apply() solutions. The calculations are mostly implemented
in C, which allow us to optimize(*) beyond what is possible to do in
plain R. The package installs out-of-the-box on all common operating
systems, including Linux, OS X and Windows.
The following example computes the median of the columns in a 20-by-500 matrix:
> library("matrixStats")
> X <- matrix(rnorm(20 * 500), nrow = 20, ncol = 500)
> stats <- microbenchmark::microbenchmark(colMedians = colMedians(X),
+ `apply+median` = apply(X, MARGIN = 2, FUN = median), unit = "ms")
> stats
Unit: milliseconds
expr min lq mean median uq max neval cld
colMedians 0.41 0.45 0.49 0.47 0.5 0.75 100 a
apply+median 21.50 22.77 25.59 23.86 26.2 107.12 100 b
It shows that colMedians() is ~51 times faster than apply(...,
MARGIN=2, FUN=median) in this particular case. The relative gain
varies with matrix shape, so you should benchmark with your
configurations. You can also play around with the benchmark reports
that are under development, e.g. html <-
matrixStats:::benchmark("colRowMedians"); !html.
WHAT IS NEW?
With this release, all the functions run faster than ever before and
at the same time use less memory than ever before, which in turn means
that now even larger data matrices can be processed without having to
upgrade the RAM. A few small bugs have also been fixed and some
“missing” functions have been added to the R API. This update is part
of a long-term tune-up that started back in June 2014. Most of the
major groundwork has already been done, but there is still room for
improvements. If you're using matrixStats functions in your package
already now, you should see some notable speedups for those function
calls, especially compared to what was available back in June. For
instance, rowMins() is now 5-20 times faster than functions such as
base::pmin.int() whereas in the past they performed roughly the same.
I've also added a large number of new package tests; the R and C
source code coverage has recently gone up from 59% to 96% (… and
counting) [https://coveralls.io/r/HenrikBengtsson/matrixStats?branch=develop].
Some of the bugs were discovered as part of this effort. Here a
special thank should go out to Jim Hester for his great work on covr
[https://github.com/jimhester/covr], which provides me with on-the-fly
coverage reports via Coveralls. (You can run covr locally or via
GitHub + Travis CI, which is very easy if you're already up and
running there. Try it!) I would also like to thank the R core team and
the CRAN team for their continuous efforts on improving the package
tests that we get via R CMD check but also via the CRAN farm (which
occasionally catches code issues that I'm not always seeing on my
end).
Footnote:
(*) One strategy for keeping the memory footprint at a minimum is to
optimize the implementations for the integer and the numeric (double)
data types separately. Because of this, a great number of data-type
coercions are avoided, coercions that otherwise would consume precious
memory due to temporarily allocated copies, but also precious
processing time because the garbage collector later would have to
spend time cleaning up the mess. The new weightedMean() function,
which is many times faster than stats::weighted.mean(), is one of
several cases where this strategy is particular helpful.
Any type of feedback, including bug reports, is always appreciated!
Thanks,
Henrik
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