What is the fastest approach to saving unique values that go into matrix multiplication (without 0)?
For example, if I have a data.table object
library(data.table)
A = data.table(j3=c(3,0,3),j5=c(0,5,5),j7=c(0,7,0),j8=c(8,0,8))
I would like to see which unique values go into A*transpose(A) (or as.matrix(A) %*% as.matrix(t(A))). Right now, I can do it using for loops as:
B=t(A)
L = list()
models = c('A1','A2','A3')
for(i in 1:nrow(A)){
for(j in 1:ncol(B)){
u = union(unlist(A[i,]),B[,j])
u = u[u!=0] # remove 0
L[[paste(models[i],models[j])]]= u
}
}
However, is there a faster and more RAM-efficient way? The output doesn't have to be a list object, as in my case, it can be a data.table (data.frame) as well. Also, the order of values is not important. For example, 3 5 8 is as good as 5 3 8, 8 5 3 etc.
Any help is appreciated.
EDIT: So as.matrix(A) %*% as.matrix(t(A)) is:
[,1] [,2] [,3]
[1,] 73 0 73
[2,] 0 74 25
[3,] 73 25 98
The first element is calculated as 3*3 0*0 0*0 8*8 = 73, the second element is 3*0 0*5 0*7 8*0 = 0, etc. I need unique numbers that go to this calculation but without 0.
Therefore outputs (saved in the list L) are:
> L
$`A1 A1`
[1] 3 8
$`A1 A2`
[1] 3 8 5 7
$`A1 A3`
[1] 3 8 5
$`A2 A1`
[1] 5 7 3 8
$`A2 A2`
[1] 5 7
$`A2 A3`
[1] 5 7 3 8
$`A3 A1`
[1] 3 5 8
$`A3 A2`
[1] 3 5 8 7
$`A3 A3`
[1] 3 5 8
Once again, the output doesn't have to be a list object. I would prefer data.table if it is doable. Is it possible to rewrite my approach as Rcpp function?
CodePudding user response：
Perhaps we can try this
AA <- replace(A, A == 0, NA)
c(t(outer(
1:nrow(A),
1:nrow(A),
Vectorize(function(x, y) unique(na.omit(c(t(AA[c(x, y)])))))
)))
which gives
[[1]]
[1] 3 8
[[2]]
[1] 3 8 5 7
[[3]]
[1] 3 8 5
[[4]]
[1] 5 7 3 8
[[5]]
[1] 5 7
[[6]]
[1] 5 7 3 8
[[7]]
[1] 3 5 8
[[8]]
[1] 3 5 8 7
[[9]]
[1] 3 5 8
CodePudding user response：
Thanks for posting the additional information in your edits. From what you posted, it appears that for all pairs of rows of a matrix or data table A, you want the unique non-zero values in those two rows.
To do that efficiently I'd suggest ensuring that A is a matrix. Row indexing in dataframes or data tables is a lot slower than doing so in matrices. (Column indexing can be faster, but I doubt if it's worth transposing the table to get that.)
Once you have a matrix, A[i, ] is a vector containing the values in row i, and that's a pretty fast calculation. You want the unique non-zero values in c(A[i, ], A[j, ]). The unique function will produce this, but won't leave out the zeros. I'd suggest experimenting. Depending on the contents of each row, it is conceivable that leaving the zeros out of the rows first before computing the unique entries could be either faster or slower than calculating all the unique values and deleting 0 afterwards.
You say you want to do this for a few hundred rows, but each row is very long. I'd guess you won't be able to improve much on nested loops: the time will be spent on each entry, not on the loops. However, you could experiment with vectorization using the apply() function, e.g.
result <- vector("list", nrows)
for (i in 1:nrows)
result[[i]] <- apply(A, 1, function(row) setdiff(unique(c(row, A[i,])), 0))
This will give a list of lists; if you want to examine entry i, j, you can use result[[c(i,j)]].
CodePudding user response：
Potential optimizations
Following up on @user2554330's answer, note that if A is an m-by-n matrix, then AAT = A %*% t(A) (equivalently tcrossprod(A)) is an m-by-m symmetric matrix. AAT[i, j] and AAT[j, i] are computed using the same entries of A, so you only need to inspect m*(m 1)/2 pairs of rows of A, not m*m.
You can do even better by finding the unique elements of each row first before pairing them. Preprocessing in this way is almost certainly worth it if your matrix indeed has 4e 07 columns.
Limitations
Another aspect of the problem is how unique works under the hood. unique has an argument nmax that you can use to specify an expected maximum number of unique elements. From ?duplicated:
Except for factors, logical and raw vectors the default
nmax = NAis equivalent tonmax = length(x). Since a hash table of size8*nmaxbytes is allocated, settingnmaxsuitably can save large amounts of memory. For factors it is automatically set to the smaller oflength(x)and the number of levels plus one (forNA). Ifnmaxis set too small there is liable to be an error:nmax = 1is silently ignored.
Long vectors are supported for the default method of
duplicated, but may only be usable ifnmaxis supplied.
These comments apply to unique as well. Since you have a 300-by-4e 07 matrix, you would be evaluating (with preprocessing):
unique(<4e 07-length vector>), 300 times,unique(<up to 8e 07-length vector>),300*301/2times.
That can consume a lot of memory if you don't know anything about your matrix that might allow you to set nmax. And it can take a long time if you don't have access to many CPUs.
So I agree with comments asking you to consider why you need to do this at all and whether your underlying problem has a nicer solution.
Two answers
FWIW, here are two approaches to your general problem that actually take advantage of symmetry. f and g are without and with preprocessing. `[[.utri` allows you to extract elements from the return value, an m*(m 1)/2-length list, as if it were an m-by-m matrix.
f <- function(A, nmax = NA) {
a <- seq_len(nrow(A))
J <- cbind(sequence(a), rep.int(a, a))
FUN <- function(i) {
if (i[1L] == i[2L]) {
x <- A[i[1L], ]
} else {
x <- c(A[i[1L], ], A[i[2L], ])
}
unique.default(x[x != 0], nmax = nmax)
}
res <- apply(J, 1L, FUN, simplify = FALSE)
class(res) <- "utri"
res
}
g <- function(A, nmax = NA) {
l <- lapply(asplit(A, 1L), function(x) unique.default(x[x != 0], nmax = nmax))
a <- seq_along(l)
J <- cbind(sequence(a), rep.int(a, a))
FUN <- function(i) {
if (i[1L] == i[2L]) {
x <- l[[i[1L]]]
} else {
x <- c(l[[i[1L]]], l[[i[2L]]])
}
unique.default(x)
}
res <- apply(J, 1L, FUN, simplify = FALSE)
class(res) <- "utri"
res
}
`[[.utri` <- function(x, i, j) {
stopifnot(length(i) == 1L, length(j) == 1L)
if (i <= j) {
x[[i (j * (j - 1L)) %/% 2L]]
} else {
x[[j (i * (i - 1L)) %/% 2L]]
}
}
Here is a simple test on a 4-by-4 integer matrix:
mkA <- function(m, n) {
A <- sample(0:(n - 1L), size = as.double(m) * n, replace = TRUE,
prob = rep.int(c(n - 1, 1), c(1L, n - 1L)))
dim(A) <- c(m, n)
A
}
set.seed(1L)
A <- mkA(4L, 4L)
A
## [,1] [,2] [,3] [,4]
## [1,] 0 0 2 3
## [2,] 0 1 0 0
## [3,] 2 1 0 3
## [4,] 1 2 0 0
identical(fA <- f(A), g(A))
## [1] TRUE
fA[[1L, 1L]] # used for 'tcrossprod(A)[1L, 1L]'
## [1] 2 3
fA[[1L, 2L]] # used for 'tcrossprod(A)[1L, 2L]'
## [1] 2 3 1
fA[[2L, 1L]] # used for 'tcrossprod(A)[2L, 1L]'
## [1] 2 3 1
fA # under the hood, a list of length 'choose(ncol(A) 1, 2)'
## [[1]]
## [1] 2 3
##
## [[2]]
## [1] 2 3 1
##
## [[3]]
## [1] 1
##
## [[4]]
## [1] 2 3 1
##
## [[5]]
## [1] 1 2 3
##
## [[6]]
## [1] 2 1 3
##
## [[7]]
## [1] 2 3 1
##
## [[8]]
## [1] 1 2
##
## [[9]]
## [1] 2 1 3
##
## [[10]]
## [1] 1 2
##
## attr(,"class")
## [1] "utri"
And here are two benchmarks on two large integer matrices, showing that preprocessing can help a lot:
set.seed(1L)
A <- mkA(100L, 1e 04L)
microbenchmark::microbenchmark(f(A), g(A), times = 10L, setup = gc(FALSE))
## Unit: milliseconds
## expr min lq mean median uq max neval
## f(A) 2352.0572 2383.3100 2435.7954 2403.8968 2431.6214 2619.553 10
## g(A) 843.0206 852.5757 858.7262 858.2746 863.8239 881.450 10
A <- mkA(100L, 1e 06L)
microbenchmark::microbenchmark(f(A), g(A), times = 10L, setup = gc(FALSE))
## Unit: seconds
## expr min lq mean median uq max neval
## f(A) 290.93327 295.54319 302.57001 301.17810 307.50226 318.14203 10
## g(A) 72.85608 73.83614 76.67941 76.57313 77.78056 83.73388 10