> On Oct 21, 2017, at 7:50 AM, Martin Maechler <maech...@stat.math.ethz.ch>
> wrote:
>
>>>>>> C W <tmrs...@gmail.com>
>>>>>> on Fri, 20 Oct 2017 15:51:16 -0400 writes:
>
>> Thank you for your responses. I guess I don't feel
>> alone. I don't find the documentation go into any detail.
>
>> I also find it surprising that,
>
>>> object.size(train$data)
>> 1730904 bytes
>
>>> object.size(as.matrix(train$data))
>> 6575016 bytes
>
>> the dgCMatrix actually takes less memory, though it
>> *looks* like the opposite.
>
> to whom?
>
> The whole idea of these sparse matrix classes in the 'Matrix'
> package (and everywhere else in applied math, CS, ...) is that
> 1. they need much less memory and
> 2. matrix arithmetic with them can be much faster because it is based on
> sophisticated sparse matrix linear algebra, notably the
> sparse Cholesky decomposition for solve() etc.
>
> Of course the efficency only applies if most of the
> matrix entries _are_ 0.
> You can measure the "sparsity" or rather the "density", of a
> matrix by
>
> nnzero(A) / length(A)
>
> where length(A) == nrow(A) * ncol(A) as for regular matrices
> (but it does *not* integer overflow)
> and nnzero(.) is a simple utility from Matrix
> which -- very efficiently for sparseMatrix objects -- gives the
> number of nonzero entries of the matrix.
>
> All of these classes are formally defined classes and have
> therefore help pages. Here ?dgCMatrix-class which then points
> to ?CsparseMatrix-class (and I forget if Rstudio really helps
> you find these ..; in emacs ESS they are found nicely via the usual key)
>
> To get started, you may further look at ?Matrix _and_ ?sparseMatrix
> (and possibly the Matrix package vignettes --- though they need
> work -- I'm happy for collaborators there !)
>
> Bill Dunlap's comment applies indeed:
> In principle all these matrices should work like regular numeric
> matrices, just faster with less memory foot print if they are
> really sparse (and not just formally of a sparseMatrix class)
> ((and there are quite a few more niceties in the package))
>
> Martin Maechler
> (here, maintainer of 'Matrix')
>
>
>> On Fri, Oct 20, 2017 at 3:22 PM, David Winsemius <dwinsem...@comcast.net>
>> wrote:
>
>>>> On Oct 20, 2017, at 11:11 AM, C W <tmrs...@gmail.com> wrote:
>>>>
>>>> Dear R list,
>>>>
>>>> I came across dgCMatrix. I believe this class is associated with sparse
>>>> matrix.
>>>
>>> Yes. See:
>>>
>>> help('dgCMatrix-class', pack=Matrix)
>>>
>>> If Martin Maechler happens to respond to this you should listen to him
>>> rather than anything I write. Much of what the Matrix package does appears
>>> to be magical to one such as I.
>>>
>>>>
>>>> I see there are 8 attributes to train$data, I am confused why are there
>>> so
>>>> many, some are vectors, what do they do?
>>>>
>>>> Here's the R code:
>>>>
>>>> library(xgboost)
>>>> data(agaricus.train, package='xgboost')
>>>> data(agaricus.test, package='xgboost')
>>>> train <- agaricus.train
>>>> test <- agaricus.test
>>>> attributes(train$data)
>>>>
>>>
>>> I got a bit of an annoying surprise when I did something similar. It
>>> appearred to me that I did not need to load the xgboost library since all
>>> that was being asked was "where is the data" in an object that should be
>>> loaded from that library using the `data` function. The last command asking
>>> for the attributes filled up my console with a 100K length vector (actually
>>> 2 of such vectors). The `str` function returns a more useful result.
>>>
>>>> data(agaricus.train, package='xgboost')
>>>> train <- agaricus.train
>>>> names( attributes(train$data) )
>>> [1] "i" "p" "Dim" "Dimnames" "x" "factors"
>>> "class"
>>>> str(train$data)
>>> Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
>>> ..@ i : int [1:143286] 2 6 8 11 18 20 21 24 28 32 ...
>>> ..@ p : int [1:127] 0 369 372 3306 5845 6489 6513 8380 8384 10991
>>> ...
>>> ..@ Dim : int [1:2] 6513 126
>>> ..@ Dimnames:List of 2
>>> .. ..$ : NULL
>>> .. ..$ : chr [1:126] "cap-shape=bell" "cap-shape=conical"
>>> "cap-shape=convex" "cap-shape=flat" ...
>>> ..@ x : num [1:143286] 1 1 1 1 1 1 1 1 1 1 ...
>>> ..@ factors : list()
>>>
>>>> Where is the data, is it in $p, $i, or $x?
>>>
>>> So the "data" (meaning the values of the sparse matrix) are in the @x
>>> leaf. The values all appear to be the number 1. The @i leaf is the sequence
>>> of row locations for the values entries while the @p items are somehow
>>> connected with the columns (I think, since 127 and 126=number of columns
>>> from the @Dim leaf are only off by 1).
>
> You are right David.
>
> well, they follow sparse matrix standards which (like C) start
> counting at 0.
>
>>>
>>> Doing this > colSums(as.matrix(train$data))
>
> The above colSums() again is "very" inefficient:
> All such R functions have smartly defined Matrix methods that
> directly work on sparse matrices.
I did get an error with colSums(train$data)
> colSums(train$data)
Error in colSums(train$data) :
'x' must be an array of at least two dimensions
Which as it turned out was due to my having not yet loaded pkg:Matrix. Perhaps
the xgboost package only imports certain functions from pkg:Matrix and that
colSums is not one of them. This resembles the errors I get when I try to use
grip package functions on ggplot2 objects. Since ggplot2 is built on top of
grid I always am surprised when this happens and after a headslap and
explicitly loading pfk:grid I continue on my stumbling way.
library(Matrix)
colSums(train$data) # no error
> Note that as.matrix(M) can "blow up" your R, when the matrix M
> is really large and sparse such that its dense version does not
> even fit in your computer's RAM.
I did know that, so I first calculated whether the dense matrix version of that
object would fit in my RAM space and it fit easily so I proceeded.
I find the TsparseMatrix indexing easier for my more naive notion of sparsity,
although thinking about it now, I think I can see that the CsparseMatrix more
closely resembles the "folded vector" design of dense R matrices. I will
sometimes coerce CMatrix objeccts to TMatrix objects if I am working on the
"inner" indices. I should probably stop doing that.
I sincerely hope my stumbling efforts have not caused any delays.
--
David.
>
>>> cap-shape=bell cap-shape=conical
>>> 369 3
>>> cap-shape=convex cap-shape=flat
>>> 2934 2539
>>> cap-shape=knobbed cap-shape=sunken
>>> 644 24
>>> cap-surface=fibrous cap-surface=grooves
>>> 1867 4
>>> cap-surface=scaly cap-surface=smooth
>>> 2607 2035
>>> cap-color=brown cap-color=buff
>>> 1816
>>> # now snipping the rest of that output.
>>>
>>>
>>>
>>> Now this makes me think that the @p vector gives you the cumulative sum of
>>> number of items per column:
>>>
>>>> all( cumsum( colSums(as.matrix(train$data)) ) == train$data@p[-1] )
>>> [1] TRUE
>>>
>>>>
>>>> Thank you very much!
>>>>
>>>> [[alternative HTML version deleted]]
>>>
>>> Please read the Posting Guide. Your code was not mangled in this instance,
>>> but HTML code often arrives in an unreadable mess.
>>>
>>>>
>>>> ______________________________________________
>>>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>> PLEASE do read the posting guide http://www.R-project.org/posti
>>> ng-guide.html
>>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>> David Winsemius
>>> Alameda, CA, USA
>>>
>>> 'Any technology distinguishable from magic is insufficiently advanced.'
>>> -Gehm's Corollary to Clarke's Third Law
>>>
>>>
>>>
>>>
>>>
>>>
>
>> [[alternative HTML version deleted]]
>
>> ______________________________________________
>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> 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
Alameda, CA, USA
'Any technology distinguishable from magic is insufficiently advanced.'
-Gehm's Corollary to Clarke's Third Law
______________________________________________
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